This paper studies a number of quantized feedback design problems for linear systems. We consider the case where quantizers are static (memoryless). The common aim of these design problems is to stabilize the given system or to achieve certain performance with the coarsest quantization density. Our main discovery is that the classical sector bound approach is nonconservative for studying these design problems. Consequently, we are able to convert many quantized feedback design problems to well-known robust control problems with sector bound uncertainties. In particular, we derive the coarsest quantization densities for stabilization for multiple-input-multiple-output systems in both state feedback and output feedback cases; and we also derive conditions for quantized feedback control for quadratic cost and H/sub /spl infin// performances.

The sector bound approach to quantized feedback control | NOVA. The University of Newcastle's Digital Repository

This paper considers actuator redundancy management for a class of overactuated nonlinear systems. Two tools for distributing the control effort among a redundant set of actuators are optimal control design and control allocation. In this paper, we investigate the relationship between these two design tools when the performance indexes are quadratic in the control input. We show that for a particular class of nonlinear systems, they give exactly the same design freedom in distributing the control effort among the actuators. Linear quadratic optimal control is contained as a special case. A benefit of using a separate control allocator is that actuator constraints can be considered, which is illustrated with a flight control example.

Resolving actuator redundancy: optimal control vs. control allocation

This note is devoted to study the stabilization problem of stochastic nonlinear delay systems with exogenous disturbances and the event-triggered feedback control. By introducing the notation of input-to-state practical stability and an event-triggered strategy, we establish the input-to-state practically exponential mean-square stability of the suggested system. Moreover, we investigate the stabilization result by designing the feedback gain matrix and the event-triggered feedback controller, which is expressed in terms of linear matrix inequalities. Also, the lower bounds of interexecution times by the proposed event-triggered control method are obtained. Finally, an example is given to show the effectiveness of the proposed method. Compared with a large number of results for discrete-time stochastic systems, only a few results have appeared on the event-triggered control for continuous-time stochastic systems. In particular, there have been no published papers on the event-triggered control for continuous-time stochastic delay systems. This note is a first try to fill the gap on the topic.

Stabilization of Stochastic Nonlinear Delay Systems With Exogenous Disturbances and the Event-Triggered Feedback Control

In this paper, the distributed adaptive event-triggered fault-tolerant consensus of general linear multiagent systems (MASs) is considered. First, in order to deal with multiplicative fault, a distributed event-triggered consensus protocol is designed. Using distributed adaptive online updating strategies, the computation of the minimum eigenvalue of Laplacian matrix is avoided. Second, some adaptive parameters are introduced in trigger function to improve the self-regulation ability of event-triggered mechanism. The new trigger threshold is both state-dependent and time-dependent, which is independent of the number of agents. Then sufficient conditions are derived to guarantee the leaderless and leader-following consensus. On the basis of this, the results are extended to the case of actuator saturation. It is proved the Zeno-behavior of considered event-triggered mechanism is avoided. At last, the effectiveness of the proposed methods are demonstrated by three simulation examples.

Distributed Adaptive Event-Triggered Fault-Tolerant Consensus of Multiagent Systems With General Linear Dynamics

Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized. Hence, this survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems. The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized stabilization of interconnected systems. Additionally, further comprehensive discussions are presented, including event-based robust control design, improvement of the critic learning rule, nonlinear $ {H_{\infty }}$ control design, and several notes on future perspectives. By applying the ADP-based optimal and robust control methods to a practical power system and an overhead crane plant, two typical examples are provided to verify the effectiveness of theoretical results. Overall, this survey is beneficial to promote the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.

Adaptive Critic Nonlinear Robust Control: A Survey

This paper is concerned with the problem of integral sliding-mode control for a class of nonlinear systems with input disturbances and unknown nonlinear terms through the adaptive actor–critic (AC) control method. The main objective is to design a sliding-mode control methodology based on the adaptive dynamic programming (ADP) method, so that the closed-loop system with time-varying disturbances is stable and the nearly optimal performance of the sliding-mode dynamics can be guaranteed. In the first step, a neural network (NN)-based observer and a disturbance observer are designed to approximate the unknown nonlinear terms and estimate the input disturbances, respectively. Based on the NN approximations and disturbance estimations, the discontinuous part of the sliding-mode control is constructed to eliminate the effect of the disturbances and attain the expected equivalent sliding-mode dynamics. Then, the ADP method with AC structure is presented to learn the optimal control for the sliding-mode dynamics online. Reconstructed tuning laws are developed to guarantee the stability of the sliding-mode dynamics and the convergence of the weights of critic and actor NNs. Finally, the simulation results are presented to illustrate the effectiveness of the proposed method.

Adaptive Actor–Critic Design-Based Integral Sliding-Mode Control for Partially Unknown Nonlinear Systems With Input Disturbances

This paper presents a novel event-triggered adaptive fuzzy fault-tolerant control approach for a class of uncertain nonlinear systems without the requirement for the online fault estimation The main objective is to guarantee the stability of the faulty systems consuming less communication resources Different from the existing results, a specific event-trigger error is designed, which is expected to reduce the amount of communications further The generalized fuzzy hyperbolic model is employed to approximate the ideal fault-tolerant control policy in the framework of event-based sampled-data control Based on the stability theory for nonlinear impulsive dynamical systems, the fuzzy adaptive control policy with a novel event-based weight update law is proposed to guarantee the stability of the closed-loop faulty system In addition, the existence of a positive lower bound of the interevent interval is ensured to avoid the Zeno phenomenon Finally, the simulation results are provided to show the effectiveness and better performance of the proposed scheme

Event-Based Fuzzy Adaptive Fault-Tolerant Control for a Class of Nonlinear Systems

A dynamic event-triggered resilient control approach to cyber-physical systems under asynchronous DoS attacks

In this paper, the problem of adaptive actor-critic (AC) tracking control is investigated for a class of continuous-time nonlinear systems with unknown nonlinearities and quantized inputs. Different from the existing results based on reinforcement learning, the tracking error constraints are considered and new critic functions are constructed to improve the performance further. To ensure that the tracking errors keep within the predefined time-varying boundaries, a tracking error transformation technique is used to constitute an augmented error system. Specific critic functions, rather than the long-term cost function, are introduced to supervise the tracking performance and tune the weights of the AC neural networks (NNs). A novel adaptive controller with a special structure is designed to reduce the effect of the NN reconstruction errors, input quantization, and disturbances. Based on the Lyapunov stability theory, the boundedness of the closed-loop signals and the desired tracking performance can be guaranteed. Finally, simulations on two connected inverted pendulums are given to illustrate the effectiveness of the proposed method.

Quantization-Based Adaptive Actor-Critic Tracking Control With Tracking Error Constraints

Summary

This paper investigates the problem of adaptive fault-tolerant control for a class of linear systems with time-varying actuator faults. The outage and loss-of-effectiveness fault cases are covered. An active fault compensation control law was designed in two steps. Firstly, the time-varying fault parameters were estimated based on a novel adaptive observer. Compared with the traditional adaptive observer, the actuator fault estimations are faster and the high-frequency oscillations can be attenuated effectively. Such oscillations are usually caused by increasing the gains of adaptive laws to deal with abrupt changes in system dynamics. Then, based on online estimations of the fault parameters, an adaptive fault-tolerant controller was constructed to compensate for the loss of actuator effectiveness and to eliminate the effect of fault estimation error. The asymptotic stability and an adaptive performance of a closed-loop system can be guaranteed, even in the case of actuator faults and disturbances. Simulation results are given to verify the effectiveness and superiority of the proposed method. Copyright © 2015 John Wiley & Sons, Ltd.

Quan-Yong Fan

Papers

Adaptive Actor–Critic Design-Based Integral Sliding-Mode Control for Partially Unknown Nonlinear Systems With Input Disturbances

Event-Based Fuzzy Adaptive Fault-Tolerant Control for a Class of Nonlinear Systems

A dynamic event-triggered resilient control approach to cyber-physical systems under asynchronous DoS attacks

Quantization-Based Adaptive Actor-Critic Tracking Control With Tracking Error Constraints

Adaptive robust actuator fault compensation for linear systems using a novel fault estimation mechanism