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This brief presents an adaptive fuzzy dynamic event-triggered tracking control scheme with guaranteed transient performance for flexible-joint (FJ) robot system. Unlike traditional backstepping control method, we approach the control problem for second-order systems based on fully actuated system approach, which greatly reduces the computational complexity. A dynamic event-triggered mechanism (DETM) is proposed that not only saves resources but also adjusts the number of triggers flexibly. In addition, we design a new time-varying smooth segmentation function and normalized function which removes the constraints on the performance function as well as achieving tracking accuracy in the desired time. At the same time, it is proved by Lyapunov stability theory that the proposed control strategy ensures that all signals of the closed-loop system are bounded and the Zeno behavior can be avoided. Finally, the proposed control strategy is applied to the 2-link FJ robot on hardware experiment platform and the feasibility and superiority of the scheme are verified.

This article addresses adaptive optimized tracking control problem for strict-feedback cyclic switched nonlinear output constrained systems with average cyclic dwell time (ACDT). Different from most of the existing results on optimized control of switched nonlinear systems, a new mode-dependent reinforcement learning (RL) algorithm of identifier-critic-actor architecture is designed. Technically, with the help of neural networks (NNs) switched state observer, the virtual and actual optimal controllers are developed by solving the Hamilton-Jacobi-Bellman (HJB) equation. Meanwhile, to reduce the impact of systems switching on the overall optimization control, the information of the switching signal is considered into the optimal performance index functions. In an attempt to settle the output constraints, a nonlinear output-dependent time-varying function is used to ensure that the system output never transgresses the prescribed regions. More importantly, by combining the improved ACDT method and Lyapunov stability theorem, a novel adaptive optimized control scheme is put forward which ensures that the boundedness of all signals in the closed-loop system. Finally, the effectiveness of the proposed optimized control algorithm is verified by numerical as well as practical simulations.

In this article, the adaptive fuzzy optimized tracking control problem of uncertain cyclic switched stochastic nonlinear systems with actuator faults and prescribed performance is studied under average cycle dwell time. For achieving a better optimal control strategy, the information of the switching signal is taken into account by the reinforcement learning algorithm of the identifier-critic-actor architecture. Meanwhile, a novel mode-dependent event-triggered optimized mechanism for subsystem is proposed to resolve the impact of asynchronous switching on system performance, which not only does not have strict assumptions but also reduces the communication burden. Moreover, to eliminate the impact caused by actuator failures, fault-tolerant compensation scheme depending on the switching signal is designed. By presenting Lemma 3 and using the coordinate transformation technique, it is proven that the choice of the performance index function still conforms to the control protocol under the prescribed performance control framework. In the optimized backstepping control design, the proposed controller is able to ensure that all signals in the closed-loop system are bounded in probability and tracking error meets performance. Finally, both numerical and practical simulations are given to illustrate the effectiveness of the proposed optimization scheme.