Adaptive decentralised dynamic surface control for non‐linear large‐scale systems against actuator failures

Adaptive actuator failure compensation control of uncertain nonlinear systems with guaranteed transient performance

SummaryThis article develops a new framework of adaptive actuator failure compensation control for cooperative manipulator systems with parameter uncertainties in addition to actuator failures, and designs and analyzes effective actuator failure compensation schemes for such robotic systems. The new adaptive control design uses an integration of multiple individual failure compensators and direct adaptation to handle various types of uncertainties in such robotic systems. The design can also be used for concurrent actuator failure cases, to expand the capability of adaptive actuator failure compensation. With a complete proof and performance analysis, it is shown that the proposed control scheme guarantees the desired closed‐loop stability and asymptotic output tracking, despite actuator failures whose patterns, time instants and values are all unknown. Simulation results of a benchmark cooperative manipulator system are presented to verify the desired control performance of the system with both typical constant and square‐wave actuator failure signals.

Actuator failure compensation for nonlinear systems which can be transformed into parametric strict-feedback form with zero dynamics is investigated. Direct adaptive compensation control schemes are developed to ensure asymptotic output tracking and stabilize the system despite the uncertainties in actuator failures as well as in system parameters. Two main cases are studied for adaptive actuator failure compensation: systems with stable zero dynamics, and systems with extra controls for stabilization. Design conditions on systems suitable for actuator failure compensation are clarified.

Distributed robust adaptive control of high order nonlinear multi agent systems

An optimal adaptive actuator failure compensation control scheme is proposed for a class of multi-input multi-output (MIMO) uncertain nonlinear continuous-time systems with redundant actuators subject to uncertain failures. Only single neural network is used in the proposed adaptive dynamic programming (ADP) scheme to simultaneously learn the infinite horizon cost function and the approximate optimal control input that minimizes the Hamilton-Jacobi-Bellman (HJB) equation. Besides, an uncertainty estimator is designed to estimate the uncertain dynamics and an adaptive parameter adjustment law is designed to estimate the actuator failure coefficients. Stability analysis and simulation results can demonstrate the effectiveness of the proposed method.

In this chapter, we further the study of adaptive actuator failure compensation control to nonlinear systems with parameter uncertainties. We fisrt develop adaptive control solutions to parametric-strict-feedback systems without zero dynamics in the presence of actuator failures in Section 10.1. The actuator failure compensation problem is characterized in terms of two actuation models, and output matching conditions for the two actuation models are derived. Adaptive compensation control schemes are developed for parametric-strict-feedback systems without zero dynamics based on matching designs for two different actuation models. Closed-loop signal boundedness and asymptotic output tracking are ensured by the developed adaptive control schemes. Simulation results for the control of an aircraft wing-rock model with augmented actuators verifies the desired performance of our adaptive designs.

ABSTRACTA new neural adaptive compensation control approach for a class of multi-input multi-output (MIMO) uncertain nonlinear systems with actuator failures is proposed in this paper. In order to enlarge the set of compensable actuator failures, an actuator grouping scheme based on multiple model switching and tuning (MMST) is proposed for the nonlinear MIMO minimum phase systems with multiple actuator failures, and RBF neural networks are used to approximate the error of plant model. Then an adaptive compensation scheme based on prescribed performance bound (PPB) which characterizes the convergence rate and maximum overshoot of the tracking error is designed for the system to ensure closed-loop signal boundedness and asymptotic output tracking despite unknown actuator failures. Simulation results are given to show the effectiveness of the control design.

An output feedback model reference adaptive controller is developed for a class of linear systems with multiple unknown time-varying state delays and in the presence of actuator failures. The adaptive controller is designed based on SPR-Lyapunov approach and is robust with respect to multiple unknown time-varying plant delays and to an external disturbance with unknown bounds. Closed-loop system stability and asymptotic output tracking are proved using suitable Lyapunov-Krasovskii functional and Simulation results are provided to demonstrate the effectiveness of the proposed controller.

Active vibration control using piezoelectric actuators in a networked and embedded environment has been widely applied to solve the rocket fairing vibration problem. However, actuator failures may lead to performance deterioration or system dysfunction. To guarantee the desired system performance, the remaining actuators should be able to coordinate with each other to compensate for the damaging effects caused by the failed actuator in a timely manner. Further, in the networked control environment, timing issues such as sampling jitter and network-induced delay should be considered in the controller design. In this study, a timing compensation approach is implemented in an adaptive actuator failure compensation controller to maintain the fairing system performance by also considering the detrimental effects from real-time constraints. In addition, time-delay compensation in the networked control system is discussed, which is able to reduce damaging effects of network-induced delays.

This paper presents an output-feedback adaptive controller for a class of linear systems with unknown time-varying state delay and in the presence of actuator failures. We consider a common type of actuator failure in which some unknown system inputs may be stuck at some unknown fixed values and at unknown time instants. The adaptive controller is designed based on SPR–Lyapunov approach for relative degree one and two cases. Closed-loop system stability and asymptotic output tracking are proved using suitable Lyapunov–Krasovskii functional for each case. Simulation results are provided to demonstrate the effectiveness of the proposed results.

Actuator failure compensation problem for nonlinear systems is formulated and adaptive actuator failure compensation control with state feedback is investigated for feedback linearizable systems. Under different structure conditions which characterize different classes of systems, adaptive state feedback control schemes are developed for systems with uncertain actuator failures. It is proved that the proposed control schemes ensure asymptotic output tracking and closed-loop signal boundedness.

An adaptive failure compensation control scheme is developed based on linear quadratic (LQ) control for discrete-time possibly nonminimum phase systems with unknown actuator failures with characterizations that some unknown system inputs are stuck at some unknown fixed or varying values at unknown time instants. The failure compensation control problem is formulated as a new LQ control problem. Based on some results on new controller structures and design conditions developed in [9], a stable adaptive law is proposed for the estimation of uncertain parameters from both the plant and actuator-failures. The adaptive failure compensation controller is implemented with adaptive parameter estimates, for achieving closed-loop stability and output regulation. Simulation results are presented to verify the desired adaptive control performance in the presence of uncertain actuator failures.

In this paper, a novel multiple-model second-level adaptive failure compensation scheme is proposed for high-speed trains with unknown failures to achieve the desired system performance. High-speed train dynamics with unknown system parameters actuator failures are first derived in detail. Multiple identification models are used to handle the parametric uncertainties and realize the desired speed and position tracking of a high-speed train with actuator failures in a first-level adaptation; identification errors of multiple identification models are used to obtain a virtual model for improving the convergence rate of parameters in a second-level adaptation. A second-level adaptive controller set is designed by using the parameters of the corresponding virtual model and the convex combination of the parameter estimates from the identification models. Finally, a switching mechanism associated with individual controllers is introduced to select the correct controller from the controller set. The stability of the closed-loop system and the performance of asymptotic state tracking are proved via the Lyapunov stability theory. Further, a simulation study was carried out by using the real line data of the CRH380A-type high-speed train from the Jinan section to the Xuzhou section. The simulation results show that in the presence of unknown actuator failures, the control scheme can control the speed tracking error within 0.4 km/h, and the switching strategy can quickly switch to the correct controller within 1 s, which verifies the effectiveness and feasibility of the proposed failure compensation scheme.

Adaptive actuator failure compensation control for hypersonic vehicle with full state constraints

This paper develops a new adaptive compensation control scheme for two linked mobile robots with actuator failurs. A configuration with two linked two-wheel drive (2WD) mobile robots is proposed, and the modelling of its kinematics and dynamics are given. An adaptive failure compensation scheme is developed to compensate actuator failures, consisting of a kinematic controller and a multi-design integration based dynamic controller. The kinematic controller is a virtual one, and based on which, multiple adaptive dynamic control signals are designed which covers all possible failure cases. By combing these dynamic control signals, the dynamic controller is designed, which ensures system stability and asymptotic tracking properties. Simulation results verify the effectiveness of the proposed adaptive failure compensation scheme.