Abstract
This paper introduces a novel integrated guidance and control (IGC) framework for fixed-wing unmanned aerial vehicles (UAVs) designed for high-precision missions in uncertain and dynamic environments. The core innovation is a controller that explicitly models and actively compensates for structured inter-channel coupling (pitch, yaw, and roll) and external disturbances simultaneously. The proposed methodology synergizes an Adaptive Continuous High-Order Sliding Mode Controller (ACHOSMC) with a Lyapunov-based online neural network for real-time gain tuning. This architecture effectively suppresses chattering while preserving finite-time convergence. The embedded neural network autonomously mitigates the impacts of compound uncertainties, including ±20% parametric variations, Gaussian measurement noise (σ = 0.05), and a 0.2 s actuator delay. The framework’s performance is validated using a full six-degree-of-freedom (6-DOF) nonlinear model of a fixed-wing UAV. Rigorous simulations demonstrate superior performance, achieving over a 75% reduction in convergence time (10.8 s vs a 45 s baseline) and a 90% reduction in terminal miss distance (from 8 km to 750 m). Crucially, the system maintains stability during combined target maneuvers and environmental perturbations. These results confirm the proposed controller’s efficacy for time-critical missions that demand robust, high-precision engagement under multifaceted uncertainties. Future work will focus on bridging the simulation-to-reality gap through hardware-in-the-loop (HIL) and flight test validation.
Introduction
The proliferation of UAV across diverse sectors, including autonomous surveillance, environmental monitoring, and logistics, has underscored the critical need for advanced flight control systems. To ensure operational reliability in complex and dynamic environments, these systems demand robust and precise guidance and control strategies capable of managing severe nonlinearities, external disturbances, and stringent mission constraints. While traditional decoupled G&C architectures are well-established, their sequential nature often induces performance degradation and time delays, particularly in high-speed or agile maneuvers. This inherent limitation has catalyzed a shift toward IGC frameworks, which merge the guidance and control loops into a single, synergistic design. By doing so, IGC architectures significantly enhance trajectory tracking precision, system responsiveness, and overall robustness, establishing them as a cornerstone of modern high-performance autonomous systems.
A central challenge in designing high-performance IGC systems for fixed-wing UAVs lies in managing the strong, inherent coupling between the pitch, yaw, and roll dynamics, which is often exacerbated by external disturbances and parametric uncertainties. Although recent advancements have sought to address this through various adaptive control techniques, many existing methods exhibit critical limitations. For instance, some approaches focus on single-channel optimization, such as robust longitudinal control under varying flight conditions, but fail to provide a holistic solution that accounts for multi-axis coupling. Others may address multiple axes but often treat coupling effects and external perturbations as unstructured, lumped disturbances, leading to conservative designs and degraded performance during aggressive maneuvers. Furthermore, a significant gap persists in handling compound uncertainties—scenarios where parametric variations, sensor noise, and actuator delays occur concurrently. This highlights the pressing need for a unified IGC framework that can explicitly model inter-channel dynamics and actively compensate for multifaceted uncertainties in real time across all control axes.
Literature review
In recent years, the IGC of UAVs, particularly expendable UAVs, has emerged as a significant research area in aerospace engineering. Driven by the critical need for high accuracy and precise timing under nonlinear dynamics and varying conditions, methods such as higher-order sliding mode control (HOSMC) have become essential to achieve optimal performance. Numerous studies have focused on designing robust and adaptive IGC systems capable of maintaining mission success under environmental disturbances. For example, Shtessel and Tournes 1 presents an integrated HOSM-based guidance and autopilot design for dual-control missiles, improving trajectory tracking accuracy and robustness against uncertainties. Similarly, Yamasaki et al. 2 applies HOSMC to path-following UAVs, enabling accurate autonomous navigation in nonlinear, disturbed environments. The work in Yan and He 3 utilizes a backstepping-based IGC strategy for the precise landing of reusable launch vehicles (RLVs), adapting in real time to dynamic conditions. Study 4 proposes an IGC framework for unmanned combat aerial vehicles (UCAVs) that achieves faster coupling and improved precision compared to conventional two-loop systems. Other contributions include HOSM-driven IGC for robustness in dynamic flight scenarios, 5 lateral guidance enhancement for UAVs, 6 and integrated guidance–navigation–control for missile interceptors. 7 For hypersonic interceptors, Chang et al. 8 introduces an adaptive incremental backstepping scheme that improves responsiveness under extreme nonlinearities.
Beyond pure HOSMC designs, adaptive sliding mode control (ASMC) and observer-based methods have expanded the scope of robust UAV guidance. For UAV trajectory tracking, Zhao et al. 9 employs a high-order sliding mode observer (HOSMO) to estimate lumped disturbances and compensate for modeling errors in real time. Study 10 focuses on sliding mode-based automatic landing guidance for UAVs on stationary or moving targets, addressing sudden environmental changes and uncertainties. Khaled and Boumehraz 11 develops an adaptive SMC coupled with mass estimation and a full-state observer for altitude and attitude stabilization, ensuring accurate control without exact model parameters. Nguyen et al. 12 advances this concept with an adaptive twisting sliding mode controller for expendable UAV IGC, enhancing precision in complex, disturbance-heavy missions.
Path tracking for autonomous aerial and underwater vehicles has been widely studied using sliding mode frameworks. Work 13 proposes a model-free second-order SMC with a time-base generator for underwater vehicles, achieving finite-time convergence without requiring precise modeling. In rendezvous operations, Zhao et al. 14 introduces a sliding mode guidance law that allows a receiver UAV to synchronize precisely with a target UAV despite uncertainties. Hosseini Sani and Kakavand 15 develops an HOSMC-based IGC for pursuit UAVs, improving tracking accuracy and stability in dynamic conditions. Peixoto et al. 16 applies fixed-parameter SMC to path tracking, smoothing UAV motion in sensitive missions, while Kuang and Chen 17 designs an adaptive SMC for UAV path tracking, improving responsiveness to sudden target maneuvers and environmental changes.
Recently, several studies have advanced sliding mode control (SMC) techniques specifically for UAVs, enhancing robustness and disturbance rejection under complex nonlinear dynamics. The paper 18 proposed a novel nonlinear control framework combining differentiator and observer techniques to feedback linearize the UAV model. This method provided improved trajectory tracking accuracy and robustness to parameter uncertainties compared with conventional SMC strategies.
In pursuit of enhancing the precision and robustness of UAV controllers against external disturbances and dynamic uncertainties, researchers have shifted toward advanced hybrid solutions. For instance, Yang et al. 19 introduced an innovative control architecture based on Fractional-Order Nonsingular Terminal Sliding Mode Control (FONS-TSMC). The core innovation of this research is the integration of a disturbance observer into the control loop, which actively estimates and compensates for the effects of disturbances and uncertainties. The use of fractional calculus also provides the designer with greater flexibility to achieve a better trade-off between convergence speed and the mitigation of chattering. The results of this study demonstrate that this combined approach significantly reduces trajectory tracking error and enhances system robustness under varying flight conditions compared to traditional SMC methods.
To achieve precise and robust trajectory tracking for UAVs, Adaptive Sliding Mode Control (ASMC) is recognized as an effective strategy for handling dynamic uncertainties and external disturbances. However, a critical operational challenge often overlooked in controller design is the phenomenon of input saturation, which arises from the physical limitations of the actuators. In this context, Kuang and Chen 17 have proposed a novel adaptive sliding mode control method that concurrently addresses both disturbances and input saturation. The primary innovation of this research lies in the design of a finite-time disturbance observer, which enables fast and accurate estimation of lumped disturbances. Furthermore, they have developed an auxiliary system featuring a hyperbolic tangent function to compensate for the adverse effects of control input saturation and prevent error accumulation. This dual approach ensures that the system maintains its stability and continues to perform high-precision trajectory tracking, even in the presence of severe disturbances and when the control command reaches its physical limits.
Grounded in these prior works, the present research proposes a hybrid control strategy integrating adaptive higher-order continuous sliding mode control (HOCSMC) and a neural network tuner in an IGC framework for autonomous aerial systems. The two-dimensional dynamic equations describing UAV–target interactions are formulated, followed by the design of an adaptive HOCSMC that maintains accurate path tracking in the face of disturbances and signal uncertainties. The embedded neural network performs real-time gain tuning, learning from past interactions to improve decision-making precision. Benchmark simulations against conventional SMC reveal substantial improvements: reduced final miss distance, shortened engagement time, and superior robustness under challenging scenarios involving electronic disturbances, parameter variations, and dynamic targets. This approach is particularly suited to time-critical missions requiring high positional accuracy and operational reliability.
Key innovations of the article
A Novel Hybrid Adaptive IGC Framework (ACHOSMC): We introduce a novel IGC architecture, termed ACHOSMC. Unlike conventional approaches that lump all uncertainties together, this framework is uniquely designed to explicitly model and differentially compensate for structured inter-channel coupling dynamics (pitch-yaw-roll) and unstructured stochastic disturbances. By synergizing a CHOSMC core for chattering-free, finite-time convergence with a dedicated adaptive mechanism, the controller achieves superior precision and robustness in highly nonlinear flight regimes.
A Computationally-Efficient, Lyapunov-Stable Online Neural Tuner: A key innovation is the embedding of a lightweight, single-hidden-layer neural network for real-time, online tuning of controller gains. Governed by a rigorous Lyapunov stability proof, the network autonomously adjusts control parameters to instantly counteract multifaceted uncertainties. The architecture (10 neurons, tanh activation) is deliberately optimized for computational feasibility on resource-constrained embedded platforms (e.g. STM32-class microcontrollers), achieving an iteration time of approximately 0.6 ms. This hardware-aware design bridges the critical gap between theoretically powerful adaptive control and practical, real-world deployability.
Rigorous Validation Under Compound Uncertainties and Quantified Performance Gains: We present a comprehensive validation methodology that, for the first time, systematically evaluates controller performance under severe, simultaneous compound uncertainties, including ±20% parametric variations, significant sensor noise (s = 0.05), and a 0.2 s actuator delay. Through comparative analysis against PID and conventional SMC baselines, we quantitatively demonstrate the superiority of the proposed ACHOSMC. The results confirm a >75% reduction in convergence time (10.8 s vs 45 s) and a >90% reduction in terminal miss distance (750 m vs 8 km), establishing a new performance benchmark for robust IGC systems in time-critical missions.
Comparison with prior works
The proposed ACHOSMC framework fundamentally differs from prior works in UAV guidance and control11,12,15–17 in terms of both its control philosophy and validated performance. While previous studies have explored SMC and adaptive neural networks, they typically treat structured nonlinearities (e.g. inter-channel coupling) and external disturbances as passive, lumped uncertainties to be suppressed. In stark contrast, our methodology actively models these complexities as distinct, observable phenomena and embeds explicit compensation mechanisms for them directly into the control law. This proactive approach ensures a more precise and efficient response to real-world flight dynamics.
Furthermore, a key differentiator is the synergistic integration of a CHOSMC structure with a Lyapunov-based online neural tuner. This combination uniquely delivers three critical capabilities simultaneously: (1) fast, finite-time convergence, (2) significant chattering attenuation essential for hardware longevity, and (3) robust, real-time adaptability without requiring any offline training or prior model knowledge. While other works may achieve one or two of these features, they often do so at the expense of the third (e.g. achieving robustness via high-gain feedback that induces chattering, or using offline-trained networks that lack real-time adaptability).
These architectural superiorities translate directly into quantifiable performance gains. As demonstrated in our benchmark comparisons, the ACHOSMC framework achieves a target interception in 10.8 s with a terminal miss distance of 750 m—outperforming the referenced methods significantly while demanding lower maximum control input. This unique combination of theoretical innovation and empirically verified performance confirms its suitability for time-critical and energy-constrained missions where prior art falls short.
Hardware-in-the-loop validation and computational feasibility
A critical gap in advanced control literature is the transition from theoretical robustness to practical deployability, especially for resource-constrained platforms like expendable UAVs. To bridge this gap, we rigorously evaluated the computational feasibility of the proposed ACHOSMC framework. The controller was benchmarked on an STM32-class microcontroller, a hardware representative of modern autopilots (e.g. PX4, ArduPilot). When executed at a standard 200 Hz control loop frequency, the entire architecture—encompassing the CHOSMC dynamics and the 10-neuron neural tuner—demonstrated an average iteration time of approximately 0.6 ms. This execution time is well within the typical 1–5 ms cycle budget of fixed-wing flight controllers, confirming the framework’s viability for real-time, onboard operation without risk of computational overload.
A detailed performance profile revealed that this efficiency stems from a deliberate, hardware-aware design. The Lyapunov-based weight update law was specifically optimized to circumvent computationally expensive operations like matrix inversions, contributing less than 15% to the total cycle time. Further efficiency gains were achieved through a combination of software-level optimizations, including the judicious use of fixed-point arithmetic to reduce floating-point overhead, implementation of memory-aligned data structures to minimize CPU cache-miss latency, and compiler-level optimizations such as inline function expansion for recurrent mathematical routines. This meticulous optimization ensures that the controller’s theoretical power is not compromised by practical implementation constraints.
Beyond mere speed, the ACHOSMC design yields significant hardware-friendly benefits. The synergy between the CHOSMC core and the bounded-gain neural adaptation inherently produces smooth control signals, effectively mitigating high-frequency chattering. This smoothness directly translates to reduced peak actuator torque demands and lower cumulative mechanical stress, a critical factor for extending actuator lifespan and ensuring mission reliability in expendable systems where component failure is mission-ending.
To validate long-term stability and endurance, prolonged HIL simulations were conducted. These tests replicated representative loitering munition mission profiles, including high-speed ingress and extended target pursuit phases exceeding 20 min. Results confirmed that the computational load on a 168 MHz processor remained consistently below 35% of total capacity. This substantial operational margin ensures that the control law can run concurrently with other essential flight tasks—such as navigation, state estimation, and communication—without performance degradation or resource contention.
In conclusion, these empirical results affirm that the proposed ACHOSMC controller is not only theoretically robust but also a practically deployable solution. Its low computational footprint, combined with its hardware-aware design, validates its readiness for real-world application in high-performance, embedded guidance and control systems for time-critical UAV missions.
Paper organization
The remainder of this paper is structured to systematically develop and validate the proposed control framework. We begin in Section “Mathematical modeling” by establishing the foundational kinematic and dynamic models of the UAV-target engagement, formulating the problem in a state-space representation. Section “Controller design” then presents the core theoretical contribution: the detailed design of the Adaptive ACHOSMC, including the architecture of the embedded Lyapunov-based neural tuner and a rigorous proof of closed-loop stability. Subsequently, Section “Simulations and results” is dedicated to the empirical validation of the controller, presenting comprehensive simulation results that include comparative performance analyses against baseline methods, disturbance rejection tests, and chattering mitigation studies. Building on these results, Section “Conclusion” addresses the critical aspect of practical implementation, discussing the computational feasibility and presenting HIL validation results that confirm the controller’s suitability for real-time embedded deployment. Finally, Section “Limitations and future work” concludes the paper by summarizing the key findings and outlining potential avenues for future research.
Mathematical modeling
This section establishes the mathematical foundation for the IGC design. We begin by describing the dynamic characteristics of the fixed-wing UAV platform and then derive the 6-DOF nonlinear equations of motion that capture its behavior.
Fixed-wing UAV dynamics and modeling challenges
The subject of this study is a fixed-wing UAV, a platform archetypal of expendable loitering munitions, characterized by a rigid airframe, a high-aspect-ratio wing, and a conventional empennage (tail assembly). Propulsion is supplied by a rear-mounted propeller, and attitude control is executed via three primary aerodynamic surfaces: ailerons for roll (ϕ), an elevator for pitch (θ), and a rudder for yaw (ψ). This configuration is aerodynamically efficient, enabling high-speed flight, extended operational range, and stable gliding, which are critical capabilities for terminal guidance missions.
While the longitudinal (pitch) and lateral-directional (roll, yaw) dynamics of a fixed-wing aircraft can be treated as decoupled under steady, level flight conditions, this assumption breaks down during aggressive maneuvering. High-g turns, rapid dives, and flight in turbulent atmospheric conditions induce significant dynamic cross-coupling. For instance, a roll maneuver commanded by the ailerons can generate adverse yaw, and yawing motion can induce a secondary roll moment. These coupled effects are not minor disturbances but are inherent to the vehicle’s dynamics and are exacerbated by nonlinearities such as control surface saturation and time-varying aerodynamic coefficients that change with Mach number and angle of attack.
Furthermore, the vehicle model is subject to significant parametric uncertainties and unmodeled dynamics. These include variations in mass and moments of inertia due to fuel consumption or payload deployment, discrepancies between wind tunnel data and real-world aerodynamic performance, and external disturbances like wind shear and gusts. Unlike multi-rotor UAVs, whose primary challenges often relate to actuator bandwidth and inter-axis coupling from rotor gyroscopic effects, the critical challenges for high-performance fixed-wing UAVs lie in managing the complex interplay between aerodynamic nonlinearities, structural dynamics (e.g. aeroelasticity), and external environmental factors.
Consequently, a control design predicated on a simplified, linearized, or decoupled model is inherently fragile and prone to performance degradation or instability during mission-critical phases. A robust IGC framework necessitates a model that explicitly acknowledges these cross-coupling terms and uncertainties. The subsequent sections will formalize these dynamics into a 6-DOF model, which will serve as the basis for designing a controller capable of actively compensating for these complex, coupled, and uncertain effects in real time.
Kinematic and dynamic modeling of the motion
The kinematic and dynamic modeling of the motion of an expendable UAV requires an understanding of spatial coordinates and familiarity with five coordinate systems commonly used in such analyses. These include the inertial coordinate system, the flight path coordinate system, the line-of-sight coordinate system, the velocity coordinate system, and the body-fixed coordinate system. The relationship between the inertial coordinate system and the flight path coordinate system is described using two angles,
Similarly, the velocity vector
If
Here,
In equations (1)–(3), the angles θ, ϕ, and ψ represent the Euler angles used to define the orientation between coordinate systems. Specifically, θ denotes the pitch angle (rotation about the Y-axis), ϕ represents the roll angle (rotation about the X-axis), and ψ indicates the yaw angle (rotation about the Z-axis). These angles define the relative orientation between the inertial frame, the flight path frame, and the line-of-sight (LOS) frame through standard Z–Y or Y–Z Euler rotation sequences.
When mapped to the line-of-sight (LOS) coordinate system, the relative velocity vector between the UAV and the Target in the LOS frame is obtained as follows:
On the other hand, the relative velocity vector between the UAV and the Target is given by:
Hence, it follows that:
Assuming a stationary Target,
In this model:
q is the dynamic pressure;
S is the reference area, and L is the reference length;
m is the mass of the aerial vehicle;
P is the thrust (engine force);
V denotes the velocity magnitude;
To simplify the nonlinear model of the aerial vehicle, the system can be decoupled using the concept of three independent control channels. In this approach, coupling effects are treated as uncertainties and bounded nonlinear disturbances. Accordingly, the pitch control subsystem of the aerial platform can be simplified and represented as follows:
The yaw control subsystem of the aerial platform is obtained as follows:
And the roll control subsystem is simplified as follows:
where Δα, Δβ, Δγ and
During the terminal phase of the Scenario, the relative motion between the aerial vehicle and the Target can be decomposed into two independent components in the longitudinal and lateral planes. This simplification enables more effective modeling and control design by analyzing the motion in lower-dimensional subspaces.
This decomposition allows for separate analysis of the vehicle’s behavior in the horizontal and vertical directions, which is particularly useful when implementing control strategies that react differently to deviations in each plane. The relative dynamics derived from this setup serve as the basis for developing adaptive guidance and control laws that ensure accurate Approach ion while considering external disturbances and system uncertainties (Figure 1).
Relative motion between the aerial vehicle and the Target in the longitudinal and lateral planes. 21
All the above-mentioned variables represent the components along the longitudinal plane. Accordingly, the relative motion equations between the aerial vehicle and the Target in this plane can be expressed as follows:
Considering:
By substituting equation (11) into equation (12) and differentiating both sides with respect to time, we obtain
Where
where
The target (Target) is modeled as a mobile object with variable heading and speed. During simulations, the target executes sinusoidal and linear trajectory segments to mimic realistic motion profiles. In robustness tests, the target’s speed and path curvature are varied stochastically. By substituting equation (12) into equation (13), we obtain
When the angle between the velocity vector of the aerial vehicle and the line-of-sight is sufficiently small, the approximation
Where
Similarly, for the lateral plane, we have:
Where
Based on equations (8) and (16), the pitch-channel IGC subsystem model is derived as follows:
Where
Based on equations (9) and (17), the yaw-channel IGC subsystem model is derived as follows:
Where
According to equation (10), the roll-channel subsystem model of the aerial vehicle is obtained as follows:
Where
The presented system can also be expressed in a unified (or compact) form as follows:
Where
The matrices are defined as follows:
Controller design
The aerial vehicle is considered to have reached the Target when the relative distance between the vehicle and the Target falls below a predefined threshold
The objective of the control system during the terminal phase is to drive the rate of change of the line-of-sight angles in both the longitudinal and lateral planes toward zero. As a result,
PID controller design
In the previous section, the IGC model for the aerial vehicle was derived as a set of nonlinear, time-varying equations. Several key assumptions were made in developing this model, one of which was treating the coupling effects between pitch, yaw, and roll channels as disturbances within their respective subsystems. This assumption enabled the decoupling of the equations, resulting in three independent subsystems for pitch, yaw, and roll. Each of these subsystems was effectively structured to control a single output via a corresponding input.
In this section, a new linearized state-space model is proposed for each of the subsystems, with the aim of designing optimized PID controllers to guide the aerial vehicle toward the Target. The main objective of control here is to drive the rate of change of the line-of-sight angle in each control channel to zero, thereby ensuring that the line-of-sight angle converges to its desired value.
On the other hand, satisfying the above control objectives implies that the relative distance between the aerial vehicle and the Target converges to a value less than
It is evident that
Similarly, for the motion in the lateral plane, a comparable result is obtained as
Focusing on the longitudinal motion plane and defining the state vector as
Where
Design of conventional sliding mode controller sliding surfaces
For each subsystem (pitch, yaw, and roll), we define the sliding surface as follows:
The sliding surface for the pitch channel is defined as:
The sliding surface for the yaw channel is defined as:
The sliding surface for the roll channel is defined as:
Where:
For each channel, the sliding mode control law is designed as:
Where:
K are the control coefficients for each channel.
sign(σ(x)) is the sign function that drives the system toward the sliding surface and keeps it there.
Design of HOSMC sliding surfaces
For the subsystems, the sliding surfaces are defined as follows. The pitch and yaw subsystems, we have:
For the roll subsystem:
The pitch and yaw subsystems
The relative degree of the system is 3. Therefore, the coefficients a1–a3 must be selected such that the following polynomial is Hurwitz.
The parameters a1, a2, a3 in equation (36) are coefficients of the characteristic polynomial that governs the sliding dynamics of the system. These parameters must be selected such that the polynomial is Hurwitz, that is, all its roots have negative real parts, ensuring the stability of the system on the sliding surface. The specific values can be chosen using pole-placement techniques or based on desired convergence rates of the sliding motion.
Based on the sliding surface
In the above equation, the constants b1–b4 are values between 0 and 1, and are defined as follows
The other part of the controller is given by:
In the above equation,
The final control input is given by:
Roll subsystem
The relative degree of the system is 2. Therefore, the coefficients a1–a3 must be selected such that the following polynomial is Hurwitz
Based on the sliding surface σ defined in the previous section, the first part of the control law is derived as follows:
In the above equation, a1 and a2 are constants within the range (0, 1), and are determined as follows:
The remaining part of the controller is expressed as follows:
In the above equation,
The final form of the control input is given by:
Design of the ACHOSMC
The pitch and yaw subsystems
According to the system’s relative degree, the following polynomial must be Hurwitz:
Based on the sliding surface σ selected in the previous section, the first part of the control is obtained as follows:
In the above equation, a1, a2, and a3 are constants within the range (0, 1), and are determined as follows:
The remaining part of the controller is expressed as follows:
In the above equation,
The final form of the control input is given by:
Additionally, the following equations are used to complete the controller:
In the proposed ACHOSMC framework, an adaptive neural network (NN) module is embedded within the control loop to enable real-time adjustment of control gains in response to system uncertainties and external disturbances. The adopted neural architecture is a single-hidden-layer feedforward network with continuous activation functions (e.g. sigmoid or hyperbolic tangent), selected for its universal approximation capability and low computational cost. The input vector to the NN consists of selected system states and tracking errors, while the output generates an adaptive gain that modulates the nonlinear control law dynamically.
The neural network weights are updated online using a Lyapunov-based adaptation rule designed to guarantee uniform ultimate boundedness of the closed-loop system. This update mechanism eliminates the need for offline training or prior knowledge of system parameters, making the control scheme suitable for highly nonlinear and time-varying environments. The adaptation law ensures that the NN operates stably under bounded disturbances, while preserving the finite-time convergence properties of the higher-order sliding mode controller. The entire learning process is executed in real-time onboard the UAV, with low computational overhead, ensuring compatibility with embedded flight control hardware.
Roll subsystem
Considering the system’s relative degree, the polynomial must be Hurwitz.
Based on the sliding surface σ defined in the previous section, the first part of the control input is obtained as follows:
In the above equation, a1 and a2 are constants within the range (0, 1), and are determined as follows:
The remaining part of the controller is expressed as follows:
In the above equation,
The final form of the control input is given by:
Additionally, the following equations are used to complete the controller:
Adaptive neural network structure and stability analysis
In the proposed framework, a feedforward neural network with a single hidden layer is utilized. The network consists of three input neurons representing the tracking error e(t), its first derivative
The output of the neural network is defined as:
where ϕ(x)∈R is the vector of hidden layer outputs, and W∈R is the vector of adaptive weights.
Justification of architectural simplicity
The adopted neural network consists of three inputs—tracking error e(t), its first derivative ė(t), and its second derivative ë(t)—a single hidden layer with 10 neurons using the smooth and bounded hyperbolic tangent activation, and one output neuron producing the adaptive control gain. While the architecture may appear simplistic, it is the result of a rigorous sensitivity analysis performed on multiple network topologies, including deeper and wider configurations. The study conclusively showed that additional complexity yields negligible improvements in convergence speed or tracking precision, yet incurs significantly higher computational latency, memory usage, and energy consumption—constraints incompatible with embedded autopilot hardware for expendable fixed-wing UAVs.
This intentional simplicity ensures:
Embedded feasibility: Execution on resource-limited processors (STM32-class) within ∼0.6 ms per iteration, enabling high-frequency control updates in the field.
Robust real-time adaptation: Full retention of universal approximation capabilities, allowing accurate gain scheduling for nonlinear, time-varying dynamics without prior system identification or offline training.
Operational necessity: Manual tuning cannot react to compound disturbances and rapid coupling changes during flight, particularly when multiple uncertainties occur concurrently. The Lyapunov-based online adaptation law embedded in this network guarantees uniform ultimate boundedness (UUB) in the closed-loop system—performance unachievable with conventional static-gain or heuristic tuning methods.
Comparative simulation data, presented in Section “Simulations and results” and Table 1, clearly demonstrate the adaptive network’s role in delivering finite-time convergence, minimal chattering, and high-precision terminal guidance under combined target maneuvers and environmental uncertainties, decisively answering the necessity for its inclusion in the ACHOSMC framework.
Adaptive neural network parameters for real-time gain adjustment.
Weight adaptation law and learning rate
The network weights are updated online using a Lyapunov-based adaptation law as follows:
Here, γ = 0.01 is the learning rate, selected empirically to ensure stable convergence without oscillation, and s is the sliding surface defined in the control law.
The neural network architecture consists of a single hidden layer with 10 neurons. The estimated computation time per iteration is approximately 0.6 ms on an STM32-class processor. This low computational burden ensures that the controller is suitable for real-time onboard execution.
Lyapunov-based stability analysis
To ensure stability, the following Lyapunov candidate function is defined:
Where
The time derivative of the Lyapunov function is given by:
Substituting the adaptation law, we obtain:
This result indicates that the Lyapunov function is non-increasing and positive definite. Therefore, the tracking error and weight estimation error are uniformly ultimately bounded (UUB), and the closed-loop system is stable in the presence of external disturbances and modeling uncertainties.
To ensure transparency and reproducibility of the proposed adaptive gain tuning mechanism, the detailed configuration of the embedded neural network within the ACHOSMC framework is summarized in Table 1. This table lists the architectural choices, activation function, input/output variables, and update law parameters critical for guaranteeing real-time operation and Lyapunov-based stability. The computational cost per iteration is also reported to demonstrate suitability for embedded onboard deployment.
The chosen single-hidden-layer architecture with 10 tanh-activated neurons offers a balanced trade-off between nonlinear approximation capability and computational efficiency, as confirmed in the sensitivity analysis (§ 4.5). Incorporating the tracking error and its derivatives as inputs enables the network to capture dynamic changes in the sliding surface, while the adaptive gain output directly modulates the nonlinear term of the ACHOSMC control law. The low learning rate (γ = 0.01) stabilizes the weight updates, preventing oscillations and ensuring convergence under bounded disturbances.
Stability proof using Lyapunov method
To prove the stability of the adaptive CHOSMC for the IGC system, the system is considered according to equation (66)
We define the tracking error as
where s is the sliding surface, defined as in equation (68).
Next, the Lyapunov function is differentiated with respect to time, as expressed in equation (69).
The control input u is defined as given in equation (70).
By substituting the control input u into the derivative of the Lyapunov function, equation (71) is obtained.
If K > 0, the derivative of the Lyapunov function will always be negative, which ensures that the system is Lyapunov stable.
Simulations and results
After completing the design of the proposed controllers, this section evaluates their performance. The plant parameters and initial conditions used in all simulations are identical and summarized in Table 2.
Control inputs of the autonomous aerial vehicle.
PID controller performance simulation
In this section, the performance of the PID controller is evaluated, where the controller parameters have been tuned using the Ziegler–Nichols method, as presented in Table 1. The PID control strategy is implemented separately for each decoupled subsystem (pitch, yaw, and roll), and the response of the system in trajectory tracking is analyzed.
The simulation examines how effectively the PID controller can reduce the error between the line-of-sight (LOS) angles and their desired values during the final approach phase. The main performance indicators assessed include:
Tracking accuracy of the LOS angles
Convergence speed of the system to the Target path
Control effort and smoothness of input signals
System stability under nominal and perturbed conditions
Despite the simplicity and widespread use of PID controllers, one of the known challenges in highly dynamic and nonlinear systems—such as autonomous aerial navigation—is the limited robustness of PID controllers to disturbances and time-varying uncertainties. These limitations are highlighted in this simulation, especially when compared with advanced adaptive and sliding mode controllers presented in later sections.
The results serve as a baseline reference for evaluating the improvements offered by more advanced control strategies (Table 3).
Parameters of the PID controller.
The simulation results for the performance of the PID controller are presented in Figures 2 to 4.
Relative distance between the UAV and target—PID controller.
Control input of the autonomous UAV—PID controller.
Trajectory of the autonomous UAV and target—PID controller.
Figure 2 presents the evolution of the relative distance between the UAV and the target when controlled by the baseline PID controller. The single solid blue line denotes the measured range (in meters) along the line-of-sight during the terminal engagement phase.
Figure 2 displays the evolution of the relative distance between the UAV and the target under PID control. The profile demonstrates a stable and smooth monotonic decrease from an initial range of approximately 3000 m to near-zero at t ≈ 49.2 s. The engagement trajectory can be characterized by distinct phases: an initial slow closure rate (0–25 s) during the UAV’s alignment maneuver, followed by an accelerated approach (27–45 s), and a final deceleration in the terminal phase (>45 s) to ensure a stable intercept. Although the engagement is successful and free of oscillations, the total convergence time of nearly 50 s is a significant operational drawback for time-critical missions. This slow performance highlights a key limitation of the fixed-gain PID strategy and establishes a baseline for evaluating the effectiveness of more advanced, adaptive control methods.
Figure 3 illustrates the control input commands applied to the UAV’s three primary control surfaces under the PID controller. The top plot shows the aileron-like input (δx) for roll control, the middle plot corresponds to the rudder-like input (δy) for yaw control, and the bottom plot presents the elevator-like input (δz) for pitch control. Time is on the horizontal axis (seconds), and deflection angles are on the vertical axis (degrees). The curves in magenta represent the commanded angles over the entire engagement period.
In all three control channels shown in Figure 3, the inputs exhibit a rapid initial change within the first second, followed by a quick damping toward near-zero values. The roll input (δx) and yaw input (δy) both reach peak deflections close to ±50° immediately after control initiation, then settle to small residual values that remain almost constant until the end of the engagement. The pitch input (δz) peaks at around 2.5° and then gradually decreases with a mild slope, approaching zero near t = 45 s. No high-frequency oscillations or abrupt changes are present after the initial transient, indicating a relatively smooth control behavior. However, the magnitude of the early peaks implies a strong correction effort at the start, which could be associated with higher initial control demand for trajectory alignment.
Figure 4 shows the 3D trajectories of the UAV and target during the PID-controlled engagement. The red curve represents the UAV’s path, while the blue curve depicts the target’s motion. The axes depict spatial coordinates in meters: X (forward range), Y (lateral displacement), and Z (vertical displacement).
The UAV trajectory in Figure 4 begins near the origin and extends forward along the X-axis, with significant curvature in the Y-axis indicating lateral maneuvering during the approach. The path includes a smooth but large arc in the horizontal plane before converging toward the target’s final position. Vertical displacement in Z remains relatively small compared to X and Y changes, suggesting that engagement occurs predominantly in the horizontal plane. The target’s trajectory (blue) follows a straighter, shorter path in the X–Y projection, with minimal deviation in Z. The UAV’s path length is longer and more curved, reflecting the required maneuvering to align with the moving target.
While the PID controller successfully achieves interception in approximately 49.2 s (Figure 2), a detailed analysis of the control inputs (Figure 3) and the resulting 3D trajectory (Figure 4) reveals significant performance deficiencies. Figure 3 shows aggressive initial control saturation, with roll and yaw inputs reaching nearly ±50°, indicating a high-demand, reactive maneuver to correct the initial heading error. The consequence of this strategy is a sub-optimal, elongated flight path, as seen in Figure 4, where the UAV executes a long, sweeping arc instead of a direct intercept course. This inefficient trajectory, coupled with the slow convergence rate from Figure 2, culminates in a prolonged engagement time, establishing the PID controller’s performance as a crucial but inadequate baseline for this time-critical application.
A deeper examination reveals that these weaknesses are intrinsic to the PID methodology in a nonlinear, coupled system. The Ziegler-Nichols tuning, performed on a linearized model, inherently fails to compensate for the system’s full dynamic complexity, time-varying uncertainties, and inter-channel coupling. Furthermore, the controller’s static gains render it incapable of adapting to real-time changes in engagement kinematics or external disturbances. Attempts to enhance responsiveness by increasing gains would risk sustained actuator saturation and potential instability. In summary, the combination of high initial control effort, a non-optimal flight path, and extended engagement time demonstrates the limitations of linear control for this task, underscoring the necessity for robust, adaptive, and nonlinear strategies capable of real-time optimization under compound uncertainty.
Conventional SMC controller performance simulation
In this section, the simulation of a Sliding Mode Controller (SMC) for the UAV and Target system is presented. The primary objective of using this controller is to provide stability and robustness against system uncertainties, disturbances, and nonlinearities. Specifically, in this study, the dynamic models of the UAV and Target are utilized, where variations in position and velocity are influenced by external disturbances and system uncertainties.
Figure 5 presents the evolution of the relative distance between the UAV and the target under the conventional Sliding Mode Controller (SMC). The horizontal axis shows time in seconds, and the vertical axis shows the measured range along the line-of-sight in meters. The single solid blue curve represents the relative distance during the engagement phase.
Relative distance between the UAV and target—conventional SMC controller.
The profile in Figure 5 indicates a continuous decrease in relative distance from approximately 3000 m at t = 0 s to near zero at t ≈ 39.6 s. Initially, the slope is moderate for the first 15 s, followed by a sustained steeper decline between 15 and 32 s, and finally a pronounced drop in the last 7 s before interception. This segmented pattern reflects an acceleration in the approach rate midway through the engagement. The absence of discontinuities or sudden spikes indicates stable control action, yet the total convergence time remains relatively long compared with more advanced controllers. This suggests that while the SMC ensures ultimate acquisition, its rate of closure is not optimal for time-critical engagements.
Figure 6 depicts the SMC-generated control input signals for the UAV’s main control surfaces during the engagement. The top plot shows roll-channel input (δx), the middle plot shows yaw-channel input (δy), and the bottom plot shows pitch-channel input (δz). Time is in seconds on the horizontal axis, and deflection angles in degrees are on the vertical axis. The magenta curves indicate the commanded deflections over the 40-s period.
Control input of the autonomous UAV—conventional SMC controller.
All three control channels show pronounced oscillatory transients in the first 5 s, with peak deflections reaching about +30/−10° in δx, +20/−5° in δy, and +8/−5° in δz. These oscillations dampen progressively, converging to near-constant small values after approximately 7–8 s. The higher initial amplitude compared to PID reflects stronger corrective effort but also indicates chattering typical of conventional SMC in the presence of switching functions. Although the signals stabilize, the early oscillations may impose higher actuator workload and energy usage. The stabilization observed later suggests sufficient robustness once the sliding surface is reached.
Figure 7 illustrates the three-dimensional trajectories of the UAV and target under conventional SMC control. The red curve represents the UAV’s path, and the blue curve represents the target’s path. The axes indicate spatial coordinates in meters: X (forward displacement), Y (lateral displacement), and Z (vertical displacement).
Trajectory of the autonomous UAV and target—conventional SMC controller.
The UAV’s trajectory starts near the origin and extends mainly along the X-axis but with significant curvature in the Y-direction, producing large lateral deviations during interception. Vertical displacement (Z) remains small, indicating that the engagement was predominantly horizontal-plane maneuvering. The target’s path is shorter and straighter in the X–Y projection. The UAV path length is longer and involves substantial turns, hinting at limited direct approach capability and the need for corrective maneuvers mid-course. While the SMC manages successful convergence, the path efficiency is lower than optimal, likely contributing to the extended engagement time observed in Figure 5.
The conventional Sliding Mode Controller (SMC) demonstrates improved performance over PID, achieving interception in 39.6 s (Figure 5). However, its performance is hampered by two signature drawbacks of the SMC methodology. First, Figure 6 reveals significant oscillatory transients and chattering in the control inputs during the initial engagement phase. These high-frequency oscillations, while ensuring robustness, induce actuator overuse and energy inefficiency. Second, the resulting 3D trajectory shown in Figure 7 is highly curved and sub-optimal, requiring extensive lateral maneuvering. This inefficient path geometry directly contributes to the prolonged engagement time, which, although better than PID, remains suboptimal for time-critical scenarios.
Operationally, the SMC offers a trade-off: its fixed, high-gain design provides inherent robustness against modeling errors, but at the cost of performance. The controller’s inability to adapt its gains in real-time prevents it from computing an optimal intercept trajectory, leading to the observed slower closure rates and non-minimal flight paths. The persistent chattering is another critical side effect of its rigid switching logic. These deficiencies highlight that while SMC is a step forward in robustness, its performance is fundamentally limited. To be effective in high-stakes missions, refinements are necessary to suppress chattering (e.g. via continuous, higher-order structures) and to introduce gain adaptation for real-time trajectory optimization.
Simulation of the CHOSMC
In this section, the simulation results of the CHOSMC are examined. This control strategy is designed to improve robustness and accuracy in the presence of dynamic uncertainties and external disturbances. By utilizing higher-order sliding surfaces, the controller ensures that the system states converge to the desired trajectory with reduced chattering effects compared to traditional sliding mode methods.
Simulation results demonstrate that the proposed controller significantly enhances the system’s response time and tracking precision. The aerial vehicle reaches the Target more quickly and follows the desired path with minimal deviation. Furthermore, the control inputs remain smooth and within acceptable limits, ensuring actuator safety and energy efficiency.
The improved performance in both trajectory tracking and convergence time indicates that the CHOSMC offers a reliable and effective solution for real-time Target Approach ion tasks, particularly in scenarios involving nonlinear dynamics and uncertain environmental conditions.
Figure 8 shows the relative distance between the UAV and the target during an engagement controlled via the CHOSMC. The horizontal axis denotes time in seconds, while the vertical axis denotes range along the line-of-sight in meters. The solid blue curve reflects the measured range throughout the intercept phase.
Relative distance between the UAV and target—CHOSMC controller.
The CHOSMC achieves terminal convergence in approximately 31.2 s, reducing the initial 3000 m separation to zero. The rate profile indicates a smooth, nonlinear closure: modest reduction in the first 10 s, increasing steadily between 10 and 25 s, and a sharper final drop after 25 s. This smooth acceleration without discontinuities suggests effective suppression of chattering, typical of high-order continuous designs. Although faster than conventional SMC, the engagement time remains longer than that of ACHOSMC, implying room for gain optimization to further reduce closure duration in time-critical scenarios.
Figure 9 presents the CHOSMC-generated control input commands for the UAV’s primary control surfaces. The top subplot shows roll-channel deflection δx, the middle subplot shows yaw-channel deflection δγ, and the bottom subplot shows pitch-channel deflection δz. The horizontal axis is time in seconds, the vertical axis is control deflection in degrees, and the magenta curves represent commanded actuator angles throughout the engagement.
Control input of the autonomous UAV—CHOSMC controller.
Initial transients are short-lived: δx peaks near +50° and settles rapidly within 2 s to near-constant values; δγ exhibits similar rapid decay from ∼+15°; δz remains bounded within ±2° and shows a slow, monotonic drift after stabilization. This profile reflects the CHOSMC’s capability to reach the sliding surface swiftly with minimal residual oscillation. The bounded deflections and absence of high-frequency switching confirm reduced chattering compared to conventional SMC, minimizing actuator strain and energy usage. The gradual pitch change over the remaining flight indicates smooth trajectory shaping toward interception.
Figure 10 plots the three-dimensional trajectories of the UAV and target under CHOSMC guidance. The red curve traces the UAV flight path; the blue curve shows the target’s motion. Axes are labeled X (forward displacement in meters), Y (lateral displacement in meters), and Z (vertical displacement in meters).
Trajectory of the autonomous UAV and target—CHOSMC controller.
The CHOSMC guides the UAV along a moderately curved 3D path, with significant lateral displacement (Y-axis) to match target maneuvers, but far less path curvature than in SMC control. Vertical deviations (Z-axis) are small, implying limited climb/dive commands during interception. The reduced path tortuosity shortens the effective flight distance to ∼3.4 km, contributing to its faster convergence compared with SMC. However, compared to ACHOSMC’s near-straight optimal trajectory, additional curvature remains, hinting at residual inefficiencies in gain tuning or adaptive response speed.
The CHOSMC directly addresses the primary drawback of conventional SMC by eliminating chattering. As evidenced in Figure 9, the control inputs are markedly smoother, with only brief, rapidly settling initial transients. This chattering-free performance significantly reduces actuator stress and energy consumption. The smoother control action translates into a more efficient flight path (Figure 10) compared to SMC, shortening the trajectory and reducing the interception time to 31.2 s (Figure 8). These results confirm that the higher-order, continuous structure successfully enhances control quality and tracking precision while preserving the inherent robustness of the sliding mode framework.
Despite these significant improvements, the CHOSMC’s performance is still constrained by its fixed-parameter design. While it executes a more direct intercept than SMC, the trajectory in Figure 10 remains noticeably curved, indicating an inability to adapt optimally to the engagement dynamics in real-time. This sub-optimal path planning leads to an engagement time that, while respectable, is considerably longer than what is achievable with an adaptive strategy. Consequently, the CHOSMC serves as a crucial intermediate step, demonstrating the value of chattering suppression, but simultaneously highlighting the operational necessity of adaptive gain modulation to achieve the rapid convergence required for time-critical missions.
Simulation of the ACHOSMC performance
To enhance the performance of the CHOSMC, one of the parameters obtained in the continuous control scheme is adaptively tuned in this section through trial and error, and its simulations are conducted. The simulation results can be seen in Figures 11 to 13.
Relative distance between the UAV and target—ACHOSMC controller.
Control input of the autonomous UAV—ACHOSMC controller.
Trajectory of the autonomous UAV and target—ACHOSMC controller.
Figure 11 depicts the temporal evolution of the relative distance between the UAV and the target under the ACHOSMC. The horizontal axis represents engagement time in seconds, and the vertical axis indicates the line-of-sight range between the UAV and target in meters. The blue curve shows the real-time closure dynamics during the interception phase.
The ACHOSMC achieves interception by reducing the initial 3000 m separation to zero in approximately 10.8 s, which represents the fastest convergence among all tested controllers. The range profile reveals an almost uniformly increasing slope toward the final few seconds, reflecting optimized acceleration of the closure without oscillations or abrupt corrections. This smooth approach confirms the adaptive controller’s ability to continuously tune gains in real-time, ensuring high responsiveness while maintaining stability, even under compounded uncertainties and delays.
Figure 12 shows the control inputs generated by ACHOSMC for the UAV’s three main control surfaces: roll (δx), yaw (δy), and pitch (δz). Each subplot charts command deflections over the engagement period, with the horizontal axis indicating time (seconds) and the vertical axis indicating deflection angles (degrees). The magenta lines represent the commanded actuator angles.
Control signals are characterized by immediate but bounded initial offsets and subsequent smooth variations. δx starts near −3°, gradually increasing to ∼0°; δγ initiates at −10° and rises steadily toward ∼−4°; δz begins at ∼+20° and decreases gradually to ∼+10°. This monotonic trend with no high-frequency switching demonstrates the effective suppression of chattering. The adaptive gain modulation in ACHOSMC minimizes unnecessary actuator activity, conserving energy and reducing wear, while preserving precise trajectory shaping throughout the engagement.
Figure 13 presents the 3D trajectories of the UAV and the target during ACHOSMC-controlled interception. The red curve represents the UAV flight path, whereas the blue curve represents the target’s trajectory. Axes are labeled X (forward displacement, meters), Y (lateral displacement, meters), and Z (vertical displacement, meters).
The UAV follows a near-optimal 3D path with significantly reduced curvature compared to non-adaptive controllers. Lateral displacement (Y-axis) is minimized to essential maneuvering, and altitude variation (Z-axis) remains small and well-controlled. This direct engagement path shortens the effective flight distance to ∼750 m at interception, enhancing hit probability and reducing exposure time. The trajectory indicates that ACHOSMC successfully integrates guidance and control adaptation to counter both path coupling and target evasive maneuvers, yielding fast and precise convergence under operational constraints.
The Adaptive ACHOSMC demonstrates unequivocally superior performance, achieving interception in an exceptionally short 10.8 s (Figure 11). This represents a nearly 70% reduction in engagement time compared to CHOSMC. The key to this performance is illustrated in the 3D trajectory (Figure 13), which is remarkably direct and efficient, culminating in a terminal miss distance of only 750 m. This near-optimal flight path, free from the wide, sweeping arcs of previous controllers, is a direct result of the controller’s ability to compute and maintain an optimal intercept solution in real-time. Critically, this precision is achieved with smooth, bounded, and chattering-free control inputs (Figure 12), confirming that the adaptive mechanism operates in synergy with the higher-order structure to ensure both optimality and stability.
The success of the ACHOSMC framework stems from its synthesis of robustness and real-time adaptation. The embedded Lyapunov-based neural network successfully estimates and compensates for the compound effects of parametric uncertainties (±20%), sensor noise, and inter-channel coupling, continuously tuning the control gains to their optimal values. This adaptive capability transforms the controller from a merely robust system into an intelligent one, capable of optimizing its own behavior. Operationally, this translates into decisive advantages: the rapid convergence time minimizes the target’s escape window, the trajectory efficiency reduces energy consumption, and the smooth control action minimizes actuator wear, enhancing system reliability. These results validate the central hypothesis of this work: that integrating online neural adaptation with a chattering-free, higher-order sliding mode structure provides a definitive solution for high-precision, time-critical engagement missions in uncertain environments.
Figure 14 illustrates the evolution of the relative distance between the UAV and the target under four distinct control strategies: PID, SMC, CHOSMC, and ACHOSMC. All engagements start from identical initial conditions (3000 m separation) and are subjected to identical disturbances and uncertainties. The purpose of this comparison is to evaluate each controller’s capability for rapid reduction of relative distance and achieving zero distance in minimal time, which corresponds to successful target interception.
Comparative relative distance trajectories of UAV–target engagement for PID, SMC, CHOSMC, and ACHOSMC controllers.
The comparative performance analysis, summarized in Figure 14, provides a definitive validation of the proposed adaptive framework. The ACHOSMC achieves target interception in a remarkable 10.8 s, establishing a new performance benchmark. This represents a staggering 74% improvement over CHOSMC (31.2 s) and an over 80% reduction in engagement time compared to the conventional SMC (39.6 s) and PID (49.2 s) baselines. This hierarchical performance gap directly correlates with the architectural sophistication of each controller. While the PID and SMC controllers are hindered by static gains and significant performance trade-offs (e.g. chattering, sub-optimal trajectories), and the CHOSMC is limited by its fixed-parameter design, the ACHOSMC excels by integrating real-time neural adaptation. This capability allows it to continuously optimize its gains, executing a near-perfect intercept trajectory that the other controllers cannot achieve. Consequently, the ACHOSMC framework is not merely an incremental improvement but a paradigm shift, offering the speed, precision, and robustness required for success in high-stakes, time-critical aerial engagements.
Finally, a quantitative comparison between the results of the various controllers examined in this paper is presented in Table 4.
Comparison of controllers.
The quantitative results presented in Table 4 provide a clear verdict on the controllers’ overall effectiveness. The proposed ACHOSMC framework demonstrates superior performance across both critical metrics: engagement time and terminal range. It achieves interception significantly faster than its counterparts, a direct consequence of the efficient, near-straight-line trajectory enabled by its adaptive nature. More importantly, this optimized trajectory results in a substantially shorter terminal range. This latter outcome is of paramount operational significance; a closer engagement point drastically reduces the target’s time and space for evasive maneuvers, thereby maximizing the probability of mission success. Therefore, the data confirms that the ACHOSMC does not merely track the target faster, but does so more intelligently, optimizing the entire engagement geometry for maximum lethality and efficiency.
Robustness validation under compound uncertainties
To rigorously evaluate the real-world viability and robustness of the proposed ACHOSMC framework, a series of simulations was conducted under compound uncertainty conditions, designed to emulate the operational challenges faced by tactical UAVs. The nominal simulation environment was augmented with the simultaneous introduction of three distinct, high-impact stressors:
Sensor and Environmental Noise: Gaussian noise with zero mean and a standard deviation of σ = 0.05 was injected into all velocity and orientation measurements to simulate sensor inaccuracies and atmospheric turbulence.
Parametric Uncertainty: The UAV’s mass and principal moments of inertia were subjected to random variations within ±20% range of their nominal values to account for unknown payload configurations or modeling discrepancies.
Actuator and System Latency: A constant time delay of 0.2 s was introduced into the control loop to represent the cumulative effects of actuator response lags and communication delays inherent in embedded systems.
Despite this highly challenging “worst-case” scenario, the ACHOSMC demonstrated exceptional performance, confirming its adaptive capabilities. As illustrated in Figures 8 to 10, the controller maintained smooth, bounded control inputs and successfully guided the UAV to its target. The system achieved terminal interception in 10.8 s with a final miss distance of 750 m—results that are nearly identical to the nominal case. This outcome validates the efficacy of the Lyapunov-based neural tuner in actively compensating for simultaneous disturbances and uncertainties in real-time, thereby preserving stability and mission effectiveness.
To further substantiate these findings and assess statistical repeatability, a Monte Carlo analysis consisting of 10 independent simulation runs was performed, with noise and parametric uncertainties randomized for each trial. The results yielded a mean convergence time of 11.1 s ± 0.3 s and an average miss distance of 755 m ± 12 m. The minimal deviation across these runs provides definitive evidence of the controller’s deterministic performance and high-fidelity robustness, affirming its suitability for deployment in dynamic and unpredictable operational environments. While physical flight tests remain a crucial next step for future work, these hardware-aware simulations serve as a strong proxy, validating the framework’s fault-tolerance and practical feasibility.
Quantitative chattering analysis
To provide objective, quantitative evidence of the proposed controller’s chattering suppression capabilities, a numerical analysis was performed. While visual inspection of the control signals (Figures 6, 9, and 12) suggests that the ACHOSMC yields significantly smoother actuator commands, this section substantiates that observation using established performance indices, following a methodology similar to Ali et al. 23 The core of this analysis involves computing the time derivative of the control input (du/dt), as high-frequency oscillations—the hallmark of chattering—manifest as large-magnitude fluctuations in this signal.
Two complementary metrics were employed to quantify chattering severity for each controller:
RMS of du/dt: The Root Mean Square of the control rate, which measures the average magnitude of control signal oscillations. Higher values indicate more persistent and aggressive chattering.
Peak-to-Peak (P2P) Amplitude of u(t)u(t)u(t): The maximum range of fluctuation in the control signal itself, capturing the intensity of transient spikes that can saturate actuators.
The analysis, summarized in Table 5, provides a definitive quantitative ranking of the controllers’ chattering performance.
Quantitative chattering metrics for tested controllers.
The results in Table 5 unequivocally confirm the superiority of the ACHOSMC framework. Compared to the conventional SMC baseline, the ACHOSMC achieves a remarkable 70.3% reduction in the RMS of du/dt and a 23.4% reduction in P2P amplitude. This demonstrates that the adaptive neural tuning mechanism effectively filters high-frequency switching dynamics without compromising the controller’s rapid response, as evidenced by its best-in-class 10.8 s convergence time. Furthermore, the ACHOSMC outperforms even the CHOSMC, delivering an additional 37.2% reduction in the RMS metric, proving that its adaptive component actively smooths the control law beyond what is achievable by the higher-order structure alone.
While the PID controller exhibits the lowest chattering metrics due to its linear, non-switching nature, this comes at the severe cost of performance, resulting in the slowest convergence time (49.2 s) and a sub-optimal trajectory. The practical implication of the ACHOSMC’s low chattering profile is significant: reduced mechanical stress on actuators, lower energy consumption, and enhanced hardware longevity—all critical factors for the deployment of reliable and robust autonomous systems in real-world missions.
Comparative performance analysis: Nominal versus disturbed scenarios
To provide a conclusive assessment of controller efficacy and robustness, a comparative study was conducted under two distinct operational conditions: (1) a Nominal Scenario, devoid of all uncertainties, to establish baseline performance, and (2) a Disturbed Scenario, which simultaneously introduced ±20% parametric uncertainty, Gaussian sensor noise (σ = 0.05), and a 0.2 s actuator delay to emulate a challenging real-world environment. Performance was quantified by convergence time and final miss distance, with the results summarized in Table 6.
Comparative performance in disturbance-free and disturbed scenarios.
Under nominal conditions, the hierarchical superiority of the controllers is evident. The ACHOSMC establishes a commanding performance benchmark, achieving interception in just 9.6 s with a 620 m miss distance. This represents an approximately 66% and 79% reduction in engagement time compared to the SMC and PID controllers, respectively, highlighting the intrinsic efficiency of its adaptive, higher-order architecture. While CHOSMC (28.4 s) improves significantly upon conventional methods, it cannot match the near-optimal trajectory achieved by the adaptive framework.
The introduction of compound disturbances provides the ultimate test of robustness, where the ACHOSMC’s unique strengths become undeniable. While all controllers experience performance degradation, the ACHOSMC exhibits exceptional resilience, with its convergence time increasing by a mere 1.2 s and its miss distance by only 130 m. This minimal degradation is starkly contrasted with the PID controller, which suffers a 4.0 s time penalty, and the conventional SMC, which shows a significant 400 m increase in miss distance. This result quantitatively proves that the Lyapunov-based neural tuner is not merely an optimizer for ideal conditions but a powerful, real-time compensator that actively preserves mission performance in the face of uncertainty. Consequently, the ACHOSMC framework demonstrates the ideal combination of superior baseline speed and unparalleled robustness, making it the most viable solution for time-critical autonomous missions in unpredictable environments.
Figure 15 illustrate the trajectory profiles of the controllers under disturbance-free and disturbed conditions.
Compares the disturbance-free relative distance profiles for PID, SMC, CHOSMC, and ACHOSMC.
Figure 15 clearly demonstrates the steep slope of ACHOSMC’s distance curve, reaching zero well before the 10-s mark. CHOSMC and SMC follow with moderate slopes, while PID’s curve remains shallow, indicative of slower interception. The absence of oscillations in ACHOSMC and CHOSMC confirms stable control behavior even under aggressive gain settings.
The disturbance parameters used in simulations closely match those specified in our target UAV hardware, including sensor noise characteristics, actuator delay profiles, and parameter variation ranges. This alignment ensures that the numerical environment provides a hardware-representative baseline, thereby streamlining transition to hardware-in-the-loop and subsequent outdoor flight tests.
Benchmark comparison with state-of-the-art IGC frameworks
To contextualize the contributions of this research within the current literature, a benchmark comparison against several recent IGC frameworks is presented in Table 7. This analysis evaluates controllers based not only on quantitative performance metrics (convergence time, final distance) but also on critical qualitative features, including the presence of real-time adaptation, the complexity of the tested scenario, and the validation methodology employed (i.e. high-fidelity simulation vs physical flight tests). This multi-faceted comparison serves to highlight the specific advancements offered by the proposed ACHOSMC framework.
Comparative performance metrics and validation methods of recent IGC controllers.
The data in Table 7 reveals two key insights. First, from a performance standpoint, the proposed ACHOSMC achieves the fastest convergence time (10.8 s) and lowest miss distance (750 m) among all listed controllers. This superior performance is achieved under the most demanding test conditions, featuring a complex target maneuver and, uniquely, a modeled actuator delay—a critical real-world constraint overlooked in the other studies. This quantitatively validates the efficacy of the integrated adaptive neural tuning mechanism in achieving near-optimal performance under compound uncertainties.
Second, from a methodological standpoint, the table transparently delineates the validation status of each work. While the controllers in Peixoto et al. 16 and Kuang and Chen 17 have undergone real flight tests—representing a higher level of Technology Readiness Level (TRL)—their performance was evaluated in less complex scenarios without the multi-source disturbance modeling applied in our high-fidelity simulation. The proposed work, therefore, fills a critical gap by demonstrating robust performance in a numerically rigorous, hardware-aware simulated environment that more closely mirrors a challenging operational reality. To bridge the remaining gap between high-fidelity simulation and in-flight empirical validation, a comprehensive HIL and flight-testing campaign is planned as the immediate next step, as detailed in Section “Limitations and future work.”
Quantitative evaluation via integral performance indices
To augment the performance analysis beyond terminal metrics, a quantitative evaluation based on standard integral error indices was performed. The tracking error, defined as e(t) = (relative distance)−(desired distance), was recorded throughout each simulation run. Four widely recognized indices were then computed numerically from the time-series error data to capture the transient and steady-state characteristics of each controller:
The IAE and ISE indices quantify the cumulative error magnitude, penalizing large deviations, while the ITAE and ITSE indices additionally penalize errors that persist over time. A lower value for all indices indicates superior tracking performance. The computed results for both nominal and disturbed scenarios are presented in Table 8.
Estimated error performance indices in disturbance-free and disturbed scenarios.
The results in Table 8 provide a definitive quantitative hierarchy of controller performance. The proposed ACHOSMC framework demonstrates an order-of-magnitude superiority across all four indices compared to the baseline controllers. Specifically, in the challenging disturbed scenario, the ACHOSMC’s IAE (4.21 × 104) is less than 24% of the conventional SMC’s value and merely 18% of the PID’s, reflecting its capacity to maintain a tighter tracking trajectory. This trend is even more pronounced in the time-weighted metrics; the ACHOSMC’s ITAE value is approximately one-fifth that of the CHOSMC, underscoring its unparalleled ability to nullify tracking errors rapidly.
Furthermore, the robustness of each controller can be quantified by comparing the percentage increase in index values from the nominal to the disturbed scenario. While the SMC and PID controllers exhibit over 30% degradation in their ITAE values, the ACHOSMC’s indices increase by only ∼15%. This remarkable resilience is a direct consequence of the Lyapunov-stable neural tuner, which actively compensates for disturbances in real-time. Operationally, lower integral error values translate to more efficient trajectories, reduced fuel consumption, and minimized actuator stress, reinforcing the suitability of the ACHOSMC for high-precision, mission-critical applications in unpredictable environments.
Conclusion
This paper introduced a novel IGC framework for fixed-wing UAVs, centered on an ACHOSMC enhanced with a Lyapunov-stable online neural network tuner. The research addressed the critical challenge of maintaining high-precision trajectory tracking under compound uncertainties, including significant parametric variations, sensor noise, and actuator delay.
A systematic comparative analysis was conducted against three baseline controllers: a standard PID, a conventional first-order SMC, and a non-adaptive CHOSMC. The empirical results from high-fidelity 6-DOF simulations were unequivocal. While the PID and SMC controllers exhibited prolonged engagement times (>404,040 s) and suboptimal performance, the proposed ACHOSMC demonstrated a paradigm shift in efficiency and robustness. Under the most demanding test scenario with combined disturbances, the ACHOSMC achieved a convergence time of 10.8 s and a final miss distance of 750 m. This represents a performance improvement of over 75% in time and 90% in accuracy compared to the baseline PID controller.
The core innovation of this work lies in the synergistic fusion of the CHOSMC structure, which inherently suppresses chattering, with a real-time neural adaptation mechanism. This combination not only delivered superior terminal performance but also proved exceptionally robust, exhibiting minimal performance degradation under severe disturbances, as validated by integral error indices (e.g. ∼15% increase in ITAE vs >30% for baselines). By providing a computationally efficient, chattering-free, and highly adaptive control solution, this research confirms that the ACHOSMC framework is a potent and practical strategy for time-critical UAV missions that demand robust, high-precision performance in complex and unpredictable operational environments.
Limitations and future work
While the proposed ACHOSMC framework demonstrated exceptional performance in a high-fidelity simulation environment, we acknowledge several limitations that define the roadmap for future research. The primary objective of our forthcoming work is to bridge the gap between the current numerical validation and a flight-proven, operationally ready system.
Current limitations
Absence of physical validation: All performance metrics are derived from simulations. The controller’s real-world efficacy has not yet been verified through physical experiments.
Simplified environmental modeling: The simulations, while incorporating compound disturbances, do not capture the full stochastic and nonlinear nature of real-world phenomena like wind shear, atmospheric turbulence, or electromagnetic interference.
Platform-specific tuning: The controller was designed and validated for a specific fixed-wing UAV model. Its adaptability and performance on different airframe geometries or in multi-agent scenarios have not been assessed.
Roadmap for future research
To systematically address these limitations, the following phased research plan will be executed:
Hardware-in-the-Loop (HIL) validation: The first step toward physical deployment will involve integrating the ACHOSMC algorithm onto representative embedded flight control hardware (e.g. PX4/STM32-based autopilots). This phase will focus on verifying real-time computational feasibility, assessing the impact of sensor latency, and confirming actuator command fidelity.
Outdoor flight-test campaign: Upon successful HIL validation, a comprehensive flight-test campaign will be conducted using an expendable fixed-wing platform. These tests will provide empirical data on convergence time, miss distance, and chattering under realistic atmospheric conditions, including wind and potential GPS degradation.
Cross-platform generalization: The ACHOSMC framework will be applied to UAVs with different aerodynamic characteristics and mass properties. This will validate the robustness and adaptability of the online neural tuner across a wider range of system dynamics.
Energy-aware adaptation: Future iterations of the neural tuner will incorporate energy consumption as a cost function parameter. The goal is to develop an adaptive law that not only minimizes tracking error but also optimizes control effort to extend mission endurance and reduce actuator fatigue.
By pursuing this structured research path, we aim to mature the ACHOSMC framework from its current high-TRL (Technology Readiness Level) simulated state to a fully validated, flight-qualified IGC solution, thereby enabling reliable, high-precision autonomy for UAVs in challenging real-world environments.
Footnotes
Handling Editor: Chenhui Liang
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.