Transactions of the Institute of Measurement and Control

The paper studies the design of interval observer–based event-triggered H ∞ controller for switched systems with frequent asynchronism. We first propose an interval observer–based event-triggered mechanism, which is used to transmit the observer states and the activated mode information for controller updating. Then, we design an interval observer–based event-triggered H ∞ controller for the considered systems. By using the controller mode–dependent Lyapunov function method, sufficient conditions for the globally exponential stability of the closed-loop switched systems are obtained under average dwell time switching law. Moreover, we prove that the interevent interval length is a positive constant, which can exclude the Zeno behavior. Compared with the literature, the paper removes the strict requirement of at most one switch between two event-triggered instants, that is, frequent switches within an interevent interval are allowed. Finally, a numerical example is given to verify the effectiveness of the proposed method.

This paper studies the exponential finite-time formation problems for a group of locomotive agents that maintain order on a circle. In an ideal environment, that is, without external disturbances, a combined protocol with linear continuous-time states and their nonlinear continuous functions is proposed. Each agent can adjust the movement speed itself by changing the exponent of the nonlinear function. The obtained closed-loop system will converge with an exponential speed when the initial states of the agents are far from the target location, and with a finite-time speed nearby. For the case with external disturbances, a combined protocol with linear continuous-time states and their sign functions is presented. Due to the discontinuity of the sign functions, the Filippov solutions are employed. The exponential finite-time circle formation will be achieved even in the existence of external disturbances. Moreover, the above result is extended to the application in circle containment, where the defenders will form a circle formation to surround the swarm of protectees. Finally, several computational simulations illustrate the validity of the proposed protocols.

There are few distributed finite-time optimization results of nonlinear multiagent systems in the published literatures, especially in the case that system nonlinearities are unknown. In this paper, distributed finite-time optimization problem is investigated for higher-order multiagent systems with uncertain nonlinearities. The agent dynamics are permitted to be heterogeneous with different nonlinearities and different orders. This problem is solved by embedded control approach-based distributed finite-time optimization algorithms, which consist of two parts. In the first part, a first-order finite-time optimal signal generator is designed, of which outputs reach the minimizer of the global cost function in finite time. In the second part, embedding the generator into the feedback loop and taking the outputs of the generator as the reference outputs of the agents, and combining finite-time control and feedback domination technique together, tracking controllers are designed for the higher-order nonlinear multiagent systems to track their local optimal reference outputs in finite time. It is rigorously proven that under the proposed distributed optimization algorithms, all the agent outputs reach a bounded neighbor region of the global minimizer in finite time. The effectiveness of the proposed control algorithms is illustrated by simulations.

For systems with higher control accuracy requirements, the requirements for convergence time are also higher, such as requiring the system to maintain stability in a finite time. This paper studies the finite-time control for input-saturated system based on the event-triggered strategy and the parametric Lyapunov equation. A finite-time state feedback controller and a finite-time output feedback controller are designed by introducing a time-varying parameter which can be obtained by solving a differential equation. The proposed methods make the closed-loop system stable in finite time and save the system resources. The designed controller overcomes the problem of slow convergence speed caused by low-gain feedback control. The numerical simulation results demonstrate the effectiveness of the proposed methods.

As a prosthesis made to compensate for the residual loss of the amputee’s limb, the shoulder disarticulation upper limb prosthesis replaces the missing arm function of the shoulder amputee to a certain extent. However, the current upper limb prosthesis mainly interacts with the outside world through the prosthetic hand for grasping and gripping, and the interaction between other parts and the environment is often neglected, which is not in line with the use habits of the human arm. To address this problem, this paper proposes a reinforcement learning–based method for controlling the forearm interaction of a shoulder-disconnected upper limb prosthesis, and analyzes and solves the forces during the interaction, reducing the impact of uncertainty on interaction actions and accelerating training while ensuring the stability of handheld items. We evaluated the performance of the control method during the interaction between the upper limb prosthesis and the external environment through simulation experiments. After the training, the bionic arm was able to push the object into the target range for different objects and pushing distance requirements, which showed the good control effect of the method. Also, the control method can be applied to improve the interaction between the robotic arm and the environment.

For batch processes with small time delays and actuator partial fault, the existing methods based on iterative learning control still have some limitations, including the conservative and computationally burdensome of stability conditions and the limited fault-tolerant control capabilities. For this background, an iterative learning robust predictive fault-tolerant control method is developed, which integrates the Lyapunov–Razumikhin function method and derives stability conditions based on robust positive definite invariant set and terminal constraint set. With small time delays, the stability conditions of the system deduced using the Lyapunov–Razumikhin function are solved at a lower computational cost, which is due to the fact that the dimensionality of the stabilization condition is directly related to the size of the time delay, and thus the small time delay implies a lower dimensionality. Especially, the computational effort for solving the stability conditions online is reduced, allowing real-time control law gains to be obtained and combined with historical batches of control inputs, reducing the learning cycles of the system, and realizing stable tracking of the setpoints within shorter operating batches. Moreover, the robust positive invariant set and the set of terminal constraints are able to constrain the state of the system within a safe range for all possible uncertainties, bounded disturbances, and faults. This makes the proposed methods based on them more robust and fault tolerant. Finally, a nonlinear batch reactor is used as an example to demonstrate the effectiveness and feasibility of the developed method.

In this paper, we pay attention to event-based iterative learning predictive control for the iterative learning system affected by network delays under two-dimensional (2D) framework. First, the well-posed iterative learning predictive system is introduced to depict the lost data induced by network delays. Subsequently, the prediction-batch–based event-triggered protocol is formulated to help alleviate the adverse influence of network delays. Based on these preparations, we attempt to solve iterative learning predictive control by virtue of the 2D system theorem. To this end, the 2D Roesser-type predictive control model is carried out to describe the iterative learning predictive system, and then, the predictive control problem is considered for such 2D Roesser predictive model. The corresponding stability criteria and controller design are achieved, which also realize the tracking control of the iterative learning system. To conclude this paper, the examples are examined to illustrate the effectiveness of the proposed approach and controller design.

This paperinvestigates the distributed consensus control for nonlinear multi-agent systems with input quantization in a directed communication topology. A nonsingular adaptive finite-time control (NAFTC) scheme is proposed by combining the filtered backstepping method with neural network control. Both chattering and singularity problems of the control signals are avoided thanks to its C 1 continuity, so that the contradiction between discontinuous- and C 0 continuous finite-time control with input quantization does not exist in our scheme. At the same time, a novel inequality for the input-output relationship of hysteresis quantizer is derived, which can simply handle the input quantization without requiring additional stability analysis. What is more, some common assumptions, such as Lipschitz assumption and Holder-continuity assumption, are not required in our scheme. Finally, stability analysis and numerical simulation are provided.

In this paper, the adaptive lateral control problem is considered for autonomous electric vehicles under denial-of-service (DoS) attacks. Autonomous electric vehicles require high-precision position and pose information for the lateral control, which can be obtained from the global positioning system (GPS). However, this kind of sensors is easily susceptible to malicious DoS attacks. To mitigate the performance degradation caused by DoS attacks on GPS, a state observer is designed based on the measurements of inertial measurement units (IMUs), such that the position and pose information of vehicles can be estimated. Then, by adopting the observer states and backstepping technique, a novel adaptive secure switching control scheme is proposed. Subsequently, a stability condition is derived, which guarantees that the tracking errors converge to a compact set, and the attack effects on system dynamic performance are weakened. The stability condition also reveals the relationships among the attack durations, design parameters, and tracking errors, which can be used to guide the selection of design parameters. Simulation results are provided to validate the effectiveness and robustness of the proposed control scheme.

This paper concentrates on the positive edge consensus in directed networks with spanning trees using output feedback protocols. First, a novel positive system observer is established, which introduces a parameter to enhance the design freedom of the observer. Furthermore, sufficient conditions are proposed, which are based solely on the network’s edge count. Specifically, improved consensus and non-negative conditions are obtained by optimizing the constraints on the eigenvalue information. Subsequently, based on positive edge-consensus design conditions, a programming algorithm is developed. Finally, the effectiveness of the proposed control protocol is verified utilizing numerical simulations.

In this paper, we study the multi-target tracking problem of networked robotic systems (NRSs) within predefined time under a directed interaction topology considering disturbances and physical parameter uncertainties. On the basis of non-singular sliding-mode surface, a new predefined-time estimator-based hierarchical control algorithm is put forward so as to address the tracking problem under the case of multiple moving targets. Using the time base generator (TBG), a distributed estimator algorithm is designed to ensure that the followers can estimate different target states within a predefined-time. Certain sufficient conditions of predefined-time multi-target tracking of NRSs are presented using the Lyapunov stability and neighbor information interaction principle. In the end, numerical simulation experiments are offered to indicate the availability and correctness of the theoretical results.

To improve the following performance in large curve path and time-varying drift angle, a reduced-order extended state observer is added to the Improved Stanley Guidance Law (EISGL). Combining with the dynamic surface control (DSC), an adaptive auxiliary system is simultaneously built to compensate for the effect of the state constraints on the system. A barrier Lyapunov function combined with a prescribed performance function is designed to constrain the heading angle error of path following. Adaptive laws are also designed to approximate errors, estimating perturbation in poor sea condition. Lyapunov stability analysis shows that all signals are semi-global coherent and eventually bounded. In conclusion, the numerical comparison simulation experiments verify that the scheme has a strong anti-jamming capability and achieves good path following in large curve path.

This paper studies the event-triggered control for uncertain switched systems under injection attacks. An adaptive event-triggered control method for neural network–approximated switched systems (NNA-SSs) is proposed. The main works are as follows: First, a neural network is introduced to approximate the uncertain nonlinear item of the systems. Second, the observer-based adaptive event-triggering (OB-AET) strategy is designed to efficiently utilize communication and computing resources. Furthermore, the closed-loop switched systems considering injection attacks are established. By utilizing the Lyapunov function method and average dwell time technique, sufficient conditions for the exponential stability of the closed-loop switched systems are given. Accordingly, the gains of the state feedback controllers and observers are solved. Finally, simulation examples are given to verify the effectiveness of the proposed method.

This paper tackles the problem of trajectory tracking for wheeled mobile robots subject to time-varying input delay and bounded external disturbances. First, we establish a dynamic model for a wheeled mobile robot under sliding and skidding conditions, and determine the maximum allowable input delay that maintains system stability using Razumikhin-type stability analysis without prior knowledge of the variation in delay. The proposed adaptive robust controller combined with super-twisting sliding mode control is resilient to disturbances such as input delay, system uncertainty, and parameter variation. The proposed adaptive law enables real-time modification of switching gain based on tracking error without predefined knowledge of uncertainty bounds. Compared with traditional sliding mode control strategies, the super-twisting algorithm can eliminate chattering phenomenon while combined robust methods further reduce modeling uncertainties’ influence on system performance. Finally, we select an appropriate Lyapunov function to analyze and prove uniformly ultimately bounded of the closed-loop system. MATLAB simulation comparison results demonstrate that this approach achieves high tracking accuracy, faster response speed, and robustness.

The track tracking effect of intelligent vehicle directly affects the safety of vehicle and passengers. In the process of intelligent vehicle track tracking, the track tracking accuracy is related to many factors such as track curvature, road friction coefficient, and longitudinal speed change. In this paper, a new lateral and longitudinal coupling control algorithm is proposed. Based on the vehicle dynamics model and the optimal control theory, combined with the fuzzy control theory, the Fuzzy Linear Quadratic Regulator (FLQR) lateral optimal controller is designed, and feed-forward control and predictive controller are added. According to the real-time tracking lateral error and fuzzy control algorithm fed back by the system, the weight coefficient of the lateral displacement deviation in the cost function is dynamically adjusted; considering the coupling effect of lateral and longitudinal controllers of vehicle trajectory tracking control, a model predictive control (MPC) longitudinal speed controller is designed based on MPC theory, considering acceleration constraint and acceleration variation constraint, and taking lateral stability as evaluation index. A joint simulation platform is built based on CarSim and Simulink. The simulation results show that the designed lateral and longitudinal coupling controller of FLQR + MPC has better track tracking accuracy and can improve the driving stability of the vehicle; finally, the tracking effect of the designed algorithm is verified by real vehicle experiments. The maximum error of the designed controller algorithm in real vehicle tracking is 0.56 m, and the tracking effect is good.

This paper studies the stability of discrete-time time-varying stochastic systems with infinite Markov switching. First, the concepts of strongly exponentially stable in mean square and exponentially stable in mean square with conditions are introduced, and their equivalent conditions are given by operator theory and stochastic analysis. Second, we introduce the Lyapunov equation related to strongly exponentially stable in mean square. Third, as an application of the proposed Lyapunov stability criterion, the relationship between the internal stability and the l 2 input–output state stability is derived for the infinite Markov jump system disturbed by finite-energy random disturbance. Finally, an example is given to verify its correctness and validity.