Data-driven-based event-triggered optimal control of unknown nonlinear systems with input constraints

Periodic event-triggered adaptive tracking control design for nonlinear discrete-time systems via reinforcement learning

In this brief, an adaptive fuzzy tracking control method is proposed for switched multi-input multi-output (MIMO) nonlinear systems with time-varying full state constrains (TFSCs) and unknown control directions. First, the fuzzy logic systems are utilized to approximate unknown dynamic functions. A tangent barrier Lyapunov function (BLF-Tan) is used to solve the problem of TFSCs, and the unknown control directions problem is addressed by applying Nussbaum-type function. Then, an adaptive tracking controller is constructed by the backstepping technique. Under the designed control scheme, all the systems signals are derived to be bounded, and the tracking error of the systems is converged to a neighborhood near zero. Finally, the simulation example illustrates the control design programme is reasonable and effective.

Adaptive Fuzzy Tracking Control of Switched MIMO Nonlinear Systems With Full State Constraints and Unknown Control Directions

In this brief, an adaptive fuzzy tracking control method is proposed for switched multi-input multi-output (MIMO) nonlinear systems with time-varying full state constrains (TFSCs) and unknown control directions. First, the fuzzy logic systems are utilized to approximate unknown dynamic functions. A tangent barrier Lyapunov function (BLF-Tan) is used to solve the problem of TFSCs, and the unknown control directions problem is addressed by applying Nussbaum-type function. Then, an adaptive tracking controller is constructed by the backstepping technique. Under the designed control scheme, all the systems signals are derived to be bounded, and the tracking error of the systems is converged to a neighborhood near zero. Finally, the simulation example illustrates the control design programme is reasonable and effective. 

This paper considers the issue of adaptive neural decentralised tracking control for a class of output-constraint switched interconnected nonlinear systems with unknown backlash-like hysteresis control input. First, neural networks (NNs) are applied to approximate unknown nonlinear functions, and an NNs switched state observer is designed to estimate unmeasured system states. Then, the dynamic surface control technique is used to avoid the influence of explosion of complexity. In addition, the problem of output constraints is solved by introducing the barrier Lyapunov functions. Based on the Lyapunov stability theory, all signals in the switched closed-loop system can be verified to be uniformly ultimately bounded under the proposed control method. Moreover, the system output can track the target trajectory well within a small bounded error. Finally, a numerical simulation result is given to illustrate the effectiveness of the adaptive decentralised control scheme.

Adaptive neural decentralised control for switched interconnected nonlinear systems with backlash-like hysteresis and output constraints

Sliding-mode surface-based adaptive actor-critic optimal control for switched nonlinear systems with average dwell time

In this article, an observer‐based adaptive fuzzy hierarchical sliding mode control (HSMC) scheme is put forward for uncertain under‐actuated switched nonlinear systems in the presence of quantized input signals. The issues of quantized input signals from a hysteretic quantizer, external disturbances, unmeasured system states and completely unknown nonlinear functions are taken into account for the under‐actuated system. First, the chattering phenomena can be effectively avoided by adopting the considered hysteretic quantizer. Then, fuzzy logic systems (FLSs) is utilized to approximate the unknown smooth nonlinear functions representing the system uncertainties. Furthermore, a switched fuzzy state observer is established to estimate the unmeasured system states. Based on the Lyapunov stability theory, the proposed control scheme can ensure the boundedness of all signals in the closed‐loop system and make the system output track given reference signals well. Finally, a numerical simulation example is given to verify the effectiveness of the proposed control scheme.

Observer‐based adaptive fuzzy hierarchical sliding mode control of uncertain under‐actuated switched nonlinear systems with input quantization

This article studies the hierarchical sliding-mode surface (HSMS)-based adaptive optimal control problem for a class of switched continuous-time (CT) nonlinear systems with unknown perturbation under an actor-critic (AC) neural networks (NNs) architecture. First, a novel perturbation observer with a nested parameter adaptive law is designed to estimate the unknown perturbation. Then, by constructing an especial cost function related to HSMS, the original control issue is further converted into the problem of finding a series of optimal control policies. The solution to the HJB equation is identified by the HSMS-based AC NNs, where the actor and critic updating laws are developed to implement the reinforcement learning (RL) strategy simultaneously. The critic update law is designed via the gradient descent approach and the principle of standardization, such that the persistence of excitation (PE) condition is no longer needed. Based on the Lyapunov stability theory, all the signals of the closed-loop switched nonlinear systems are strictly proved to be bounded in the sense of uniformly ultimate boundedness (UUB). Finally, the simulation results are presented to verify the validity of the proposed adaptive optimal control scheme.

Hierarchical Sliding-Mode Surface-Based Adaptive Actor-Critic Optimal Control for Switched Nonlinear Systems With Unknown Perturbation.

This paper studies the adaptive fully distributed consensus problem for a class of nonlinear multi-agent systems (MASs) whose structures are heterogeneous, where agents with first-order dynamics and second-order dynamics exist simultaneously. Moreover, each agent subjects to Bouc-Wen hysteresis input. Due to the changes of the internal and external environment, all agents are considered to possess multiple modes and display the switching characteristic. To achieve the consensus tracking control of the considered MAS, first, the totally unknown nonlinear functions representing the system uncertainties are approximated by utilizing radial basis function neural networks (RBFNNs). Then, an adaptive neural consensus control protocol, which can enable the consensus objective to be achieved for the MAS, is designed via using the fully distributed design scheme and adaptive neural control method. It is noted that the information on the number of agents and the interaction topology is not required during the protocol design. Based on the Lyapunov stability theory, the proposed consensus protocol can ensure all signals of the considered MAS is bounded. Finally, simulation results are presented to show the feasibility and effectiveness of the proposed consensus protocol.

Haoyan Zhang

Papers

Sliding-mode surface-based adaptive actor-critic optimal control for switched nonlinear systems with average dwell time

Adaptive neural decentralised control for switched interconnected nonlinear systems with backlash-like hysteresis and output constraints

Observer‐based adaptive fuzzy hierarchical sliding mode control of uncertain under‐actuated switched nonlinear systems with input quantization

Hierarchical Sliding-Mode Surface-Based Adaptive Actor-Critic Optimal Control for Switched Nonlinear Systems With Unknown Perturbation.

Fully Distributed Consensus of Switched Heterogeneous Nonlinear Multi-Agent Systems With Bouc-Wen Hysteresis Input