Wang Xuerao, Wang Qingling, Sun Changyin
IEEE Trans Neural Netw Learn Syst. 2022 Sep;33(9):4479-4490. doi: 10.1109/TNNLS.2021.3057482. Epub 2022 Aug 31.
This article studies the prescribed performance fault-tolerant control problem for a class of uncertain nonlinear multi-input and multioutput systems. A learning-based fault-tolerant controller is proposed to achieve the asymptotic stability, without requiring a priori knowledge of the system dynamics. To deal with the prescribed performance, a new error transformation function is introduced to convert the constrained error dynamics into an equivalent unconstrained one. Under the actor-critic learning structure, a continuous-time long-term performance index is presented to evaluate the current control behavior. Then, a critic network is used to approximate the designed performance index and provide a reinforcement signal to the action network. Based on the robust integral of the sign of error feedback control method, an action network-based controller is developed. It is shown by the Lyapunov approach that the tracking error can converge to zero asymptotically with the prescribed performance guaranteed. Simulation results are provided to validate the feasibility and effectiveness of the proposed control scheme.
本文研究了一类不确定非线性多输入多输出系统的规定性能容错控制问题。提出了一种基于学习的容错控制器，以实现渐近稳定性，而无需系统动力学的先验知识。为了处理规定性能，引入了一种新的误差变换函数，将约束误差动态转换为等效的无约束误差动态。在演员-评论家学习结构下，提出了一个连续时间长期性能指标来评估当前的控制行为。然后，使用评论家网络来逼近设计的性能指标，并向动作网络提供强化信号。基于误差反馈控制方法符号的鲁棒积分，开发了一种基于动作网络的控制器。通过李雅普诺夫方法表明，跟踪误差可以渐近收敛到零，并保证规定的性能。提供了仿真结果以验证所提出控制方案的可行性和有效性。
