Adaptive Multigradient Recursive Reinforcement Learning Event-Triggered Tracking Control for Multiagent Systems

Observer-Based Neural Control of N-Link Flexible-Joint Robots

This article concentrates on the adaptive neural control approach of n -link flexible-joint electrically driven robots. The presented control method only needs to know the position and armature current information of the flexible-joint manipulator. An adaptive observer is designed to estimate the velocities of links and motors, and radial basis function neural networks are applied to approximate the unknown nonlinearities. Based on the backstepping technique and the Lyapunov stability theory, the observer-based neural control issue is addressed by relying on uplink-event-triggered states only. It is demonstrated that all signals are semi-globally ultimately uniformly bounded and the tracking errors can converge to a small neighborhood of zero. Finally, simulation results are shown to validate the designed event-triggered control strategy.

Fuzzy adaptive event-triggered distributed control for a class of nonlinear multi-agent systems

In this work, we examine an adaptive and event-triggered distributed controller for nonlinear multi-agent systems (MASs). Second, we present a fuzzy adaptive event-triggered distributed control approach using a Lyapunov-based filter and the backstepping recursion technique. Next, the controller and adaptive rule presented guarantee that all tracking errors between the leader and the follower converge in a limited area close to the origin. According to the Lyapunov stability theory, this demonstrates that all other signals inside the closed loop are assured to be semi-globally, uniformly and finally constrained. Finally, simulation tests are conducted to illustrate the effectiveness of the control mechanism.

Asynchronous Resilent State Estimation of Switched Fuzzy Systems With Multiple State Impulsive Jumps

This work researches the resilent mixed H∞ and energy-to-peak filter design problem of switched Takagi-Sugeno (T-S) fuzzy systems with asynchronous switching and multiple state impulsive jumps. The novelties include three points. First, a novel mixed H∞ and energy-to-peak performance index is proposed, which covers the H∞ performance index and energy-to-peak performance index as special cases. Second, in addition to designing the switching filters, the filter state jump rules are constructed at filter switching instants. Finally, both system states and filter states jump in a asynchronous manner. The switching law is devised through the mode-dependent average dwell time (MDADT) approach. A new type of Lyapunov-like functionals is constructed, which will increase when the subsystem is running with its mismatched filter and jump while the subsystem or the filter is switching. Then, new conditions are deduced to ensure the filtering error systems with multiple state impulsive jumps to be asymptotically stable with a mixed H∞ and energy-to-peak performance level. Filter design conditions expressing as linear matrix inequality (LMI) are obtained. Finally, the effectiveness of the derived results is illustrated by two examples.

Dynamic event-triggered controller design for nonlinear systems: Reinforcement learning strategy

The current investigation aims at the optimal control problem for discrete-time nonstrict-feedback nonlinear systems by invoking the reinforcement learning-based backstepping technique and neural networks. The dynamic-event-triggered control strategy introduced in this paper can alleviate the communication frequency between the actuator and controller. Based on the reinforcement learning strategy, actor-critic neural networks are employed to implement the n-order backstepping framework. Then, a neural network weight-updated algorithm is developed to minimize the computational burden and avoid the local optimal problem. Furthermore, a novel dynamic-event-triggered strategy is introduced, which can remarkably outperform the previously studied static-event-triggered strategy. Moreover, combined with the Lyapunov stability theory, all signals in the closed-loop system are strictly proven to be semiglobal uniformly ultimately bounded. Finally, the practicality of the offered control algorithms is further elucidated by the numerical simulation examples.