Transactions of the Institute of Measurement and Control

This paper studies the improved analysis and H_∞ control for a class of networked control systems with time-varying delays and packet dropout via a quadratic convex combination approach. The newly proposed augmented Lyapunov–Krasovskii functional is constructed by using the quadratic terms multiplied by a third-degree scalar function. A sufficient condition for asymptotic stability of networked control system is derived in terms of linear matrix inequalities. The H_∞ state feedback controller is obtained with an iteration algorithm. Differently from previous results, our derivation applies the idea of a second-order convex combination and the estimation of cross items. This method gives a reduced conservatism without using Jensen’s inequality. Numerical examples show the validity and feasibility of the proposed theoretical results.

This paper deals with the systematic design and hardware co-simulation of a fuzzy gain-scheduled proportional–integral–derivative (GS-PID) controller for a quad tilt wing (QTW) type of unmanned aerial vehicles (UAVs) based on different variants of the particle swarm optimization (PSO) algorithm. The fuzzy PID gains scheduling problem for the stabilization of the roll, pitch and yaw dynamics of the QTW vehicle is formulated as a constrained optimization problem and solved thanks to improved PSO algorithms. PSO algorithms with variable inertia weight (PSO-In), PSO with constriction factor (PSO-Co) and PSO with possibility updating strategies (PSO-gbest) are proposed. Such variants of the PSO algorithm aim further to improve the exploration and exploitation capabilities of such a stochastic algorithm as well as its convergence fastness. The robustness of the designed PSO-based fuzzy GS-PID controllers under actuators faults is shown on the non-linear model of the QTW. All optimized fuzzy GS-PID controllers are then co-simulated within a processor-in-the-loop (PIL) framework based on an embedded NI myRIO-1900 board and a host PC. Such a proposed software (SW) and hardware (HW) computer aided design (CAD) platform is based on the Control Design and Simulation (CDSim) module of the LabVIEW environment as well as a set-up Network Streams-based data communication protocol. Demonstrative simulation results are presented, compared and discussed in order to improve the effectiveness of the proposed PSO-based fuzzy gains scheduled PID controllers for the QTW’s attitude flight stabilization.

Recently, a great deal of effort has been devoted to the design of robust control techniques that compensate for lumped disturbances in mechanical robots and general electromechanical systems through disturbance observers. In this paper, assuming the Hamiltonian structure of Euler–Lagrange systems subject to a wider class of disturbances, and by exploiting some essential properties of fractional-order integro-differential operators, such as heritage and memory, a disturbance observer that is theoretically exact is proposed based on continuous fractional sliding modes, where exactness is understood in the sense of equality, in contrast to simple equivalence. The novelty of the proposal arises from the fact that the continuous fractional sliding-mode disturbance observer is exact, assuring finite-time disturbance estimation, in contrast with a classical integer-order sliding motion that is equivalent. Consequently, there arises a disturbance observer in finite time, including exact observation of continuous but not necessarily differentiable Hölder disturbances, as well as a clear compromise between regularity and robustness, which stands for a quite important issue overlooked in the conventional integer-order case. Representative simulations are discussed to highlight the reliability of the proposed method.

This paper focuses on the tracking control problem for strict-feedback nonlinear systems subject to asymmetric time-varying full state constraints. Time-varying asymmetric barrier Lyapunov functions are employed to ensure time-varying constraint satisfaction. By allowing the barriers to vary with the desired trajectory in time, the initial condition requirements are relaxed. High-order coupling terms caused by backstepping are cancelled through a novel variable substitution for the first time. Besides the normal case, where the full knowledge of the system is available, we also handle scenarios of parametric uncertainties. Asymptotic tracking is achieved without violation of any constraints, and all signals in the closed-loop system are ultimately bounded. State-constrained systems with input saturation and bounded disturbances are also considered; the tracking error converges to a bounded set around zero. The performance of the asymmetric-barrier-Lyapunov-function-based control is illustrated through a numerical example.

Three-dimensional (3D) shape measurement based on hue-height mapping using color-encoded fringe projection is proposed for thin objects. The projected color-encoded fringe pattern is encoded by three sinusoidal fringe patterns with 2π/3 shift phase in between into red (R), green (G) and blue (B) channels, separately. It is found that the hue component is of periodicity with the same period as the sinusoidal fringe pattern in the R channel of the color-encoded fringe and the hue distribution in a period is of monotonicity. While this color-encoded fringe pattern is projected onto an object, as long as the deformation of the captured deformed color fringe pattern is not out of a period, the 3D shape of the measured object can be reconstructed directly by hue-height mapping. This method is concise and fast with no color calibration to remove color crosstalk and no time-consuming phase extraction or phase unwrapping. The experimental results show the feasibility and the practicability of the proposed method.

This paper deals with the stabilization of state-constrained linear parameter-varying systems subject to parameter uncertainties and input saturation. Based on a class of parameter-dependent Lyapunov functions, and the set invariance, sufficient conditions for the stabilization problem of the linear parameter-varying systems are established in terms of parameterized linear matrix inequalities. Further, these conditions are converted into linear matrix inequalities by using a parameter relaxation technique. Finally, detailed simulation results are presented to illustrate the effectiveness of the proposed methodology.

The emergence of dynamic wireless charging technologies brings about new possibilities for on-road real-time charging of electric vehicles in solving the battery bottleneck for the mass roll-out of electric vehicles worldwide. In this new area, charging efficiency is one of the most important issues to be addressed for on-road wireless charging. While most current research mainly focuses on the electronic power design of the charging system, little has been done to improve charging efficiency through real-time mechanical control. In this paper, a switch control strategy based on an event-triggered mechanism is proposed, to improve the charging efficiency when an electric vehicle moves along a power supply road track. An H ∞ control problem is formulated and sufficient stabilization criteria are derived in the form of linear matrix inequalities when the electric vehicle derails from the effective charging range. Numerical simulation confirms that the proposed control approach outperforms the general state feedback control method. The developed control strategy is applied to control a newly built electric vehicle wireless charging test platform with desirable control performance.

In this paper, the problem of optimal preview control is studied for a class of linear continuous-time large-scale systems. We first construct an augmented system including the error signal and the reference signal to transform the tracking problem into the regulator problem. Then, the controllers are designed for isolated augmented subsystems, which also constitute the controller of large-scale systems. On the basis of proving the asymptotic stability of closed-loop large-scale systems and the existence of the controller, sufficient conditions for reaching optimal preview control are given. In particular, the limiting condition of the correlation matrices is determined by the fact that the total derivative of a positive definite Lyapunov function is negative definite. The numerical simulation indicates that the controller can drive the large-scale systems to track the reference signal without steady-state error, and the tracking effect is improved with the increasing preview length.

Traditional fault diagnosis methods mainly depend on the vector model to describe a signal, which will lead to information loss and the curse of dimensionality. In order to overcome these problems, in this paper an improved multi-linear subspace (MLS) method and locally linear embedding (LLE) are integrated (MLSLLE) to extract significant features. To obtain more information, first it is suggested that multiple sensors should be used to sample the vibration signal of a machine from different positions; then, these data are projected into different subspaces, where each sample is represented as a tensor form, respectively; finally, higher-order singular value decomposition and LLE are introduced to extract significant features. Thus a fault diagnosis method is proposed based on MLSLLE and support vector machines. The advantages of the proposed fault diagnosis method are validated by two real bearing data sets.

The sampled-data admissibility problem for nonlinear singular systems in Takagi–Sugeno fuzzy models is discussed. By adding some novel terms, a novel fuzzy time-dependent Lyapunov–Krasovskii functional is proposed to fully capture the available characteristics of the actual sampling pattern. Sufficient conditions are derived to determine the regularity, absence of impulses and asymptotic stability of the system by using Lyapunov stability theorems. Then, the fuzzy sampled-data controller is obtained by analysing the admissible condition. One practical example involving a truck–trailer system is considered. It is shown that the proposed method and the designed controller for the system are effective and that less conservativeness can be obtained.

This paper considers global asymptotic stabilization in probability for a class of cascaded stochastic nonlinear systems. The uncertainties do not meet the general matched conditions, and only boundedness of the uncertainties is assumed. Drawing on the stochastic asymptotic stability theorem, a novel robust controller is constructed based on the state transformation technique and the gradient method, which ensures that the closed-loop systems are globally asymptotically stable in probability. A numerical example is given to illustrate the effectiveness of the result.

This paper presents an uncalibrated visual servoing control system based on the human–robot–robot cooperation (HRRC). In case of malfunctions of the joint sensors of a robotic manipulator, the proposed system enables the mobile robot to continue operating the manipulator to complete the task that requires careful handling. With the aid of a virtual exoskeleton, an operator may use a human–computer interaction (HCI) device to guide the malfunctioning manipulator. During the guiding process, the virtual exoskeleton serves as a connector between the HCI device and the manipulator. However, when using the HCI device to guide the virtual exoskeleton, there could be a risk of a large-residual problem at any time caused by non-uniform guiding. To solve this problem, a residual switching algorithm (RSA) has been proposed that can identify whether the residual should be calculated based on the motion characteristics of the artificial guiding, reducing the computational cost and ensuring the tracking stability. To enhance the virtual exoskeleton’s ability to drive the manipulator, a multi-joint fuzzy driving controller has been proposed, which can drive the corresponding joint of the manipulator in accordance with an offset vector between the virtual exoskeleton and the manipulator. Lastly, the guiding experiments have verified that, compared with the contrast algorithm, the proposed RSA has a better tracking performance. A peg-in-hole assembly experiment has shown that the proposed control system can assist the operator to control efficiently the robotic manipulator with malfunctioning joint sensors.

This paper aims to develop an active disturbance rejection controller design scheme for compass-like biped robots. In the previous study, with a special designed mechanical structure on compass-like biped, we have generated a high-efficient walking gait. The original controller applied linearization approximation based on the method transverse coordinate control, with which the ability of disturbance rejection is insufficient. We introduce the active disturbance rejection controller method into the control scheme, which only requires the information of angular momentum and has the ability to compensate the model error and the external disturbance. With the new control strategy, the linearization approximation method is replaced by an active disturbance rejection controller and the robustness is improved. In order to further apply this control method, a switching control strategy has been proposed and a series of numerical simulations show that active disturbance rejection controller has a good disturbance rejection effect.

The paper mainly deals with the problem of finite-time stabilization of linear time-varying systems. A dynamic output feedback controller is designed, which is able to stabilize the linear time-varying systems in finite time. By virtue of extended piecewise constant method, novel criteria for the existence of a dynamic output feedback controller is established in terms of linear matrix inequalities. Compared with the existing method, the proposed method is more efficient from a computational point of view. A simulation is given to illustrate the effectiveness of the obtained result.