Observer‐based optimal fault‐tolerant tracking control for input‐constrained interconnected nonlinear systems with mismatched disturbances

Shihui Liu, Ning Xu, Ning Zhao, Liang Zhang
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Abstract

SummaryThis paper investigates an observer‐based optimal fault‐tolerant tracking control problem for interconnected nonlinear systems with input constraints and mismatched disturbances via adaptive dynamic programming (ADP). Firstly, an augmented system consisting of tracking error and the reference trajectory is constructed, and then the original optimal tracking control problem is transformed into the optimal regulation control problem of the augmented system. An integral sliding‐mode‐based optimal fault‐tolerant control scheme is developed to eliminate the effect of actuator faults and guarantee the optimal control performance. Furthermore, a neural network‐based observer is designed to identify the completely unknown system dynamics based on the input‐output data, thereby relaxing the restriction on system dynamics. Subsequently, the modified Hamilton‐Jacobi‐Bellman equations are solved by using the ADP algorithm under a critic network framework. According to the Lyapunov approach, all signals in the closed‐loop augmented system are uniformly ultimately bounded. Finally, the effectiveness of the developed control scheme is demonstrated via simulation results.
基于观测器的输入受限互联非线性系统最佳容错跟踪控制与不匹配干扰
摘要 本文通过自适应动态编程(ADP)研究了基于观测器的具有输入约束和不匹配干扰的互连非线性系统的最优容错跟踪控制问题。首先,构建一个由跟踪误差和参考轨迹组成的增强系统,然后将原最优跟踪控制问题转化为增强系统的最优调节控制问题。开发了基于滑模的积分最优容错控制方案,以消除执行器故障的影响并保证最优控制性能。此外,还设计了一种基于神经网络的观测器,可根据输入输出数据识别完全未知的系统动态,从而放宽对系统动态的限制。随后,在批判网络框架下使用 ADP 算法求解修正的汉密尔顿-雅各比-贝尔曼方程。根据 Lyapunov 方法,闭环增强系统中的所有信号最终都是均匀有界的。最后,通过仿真结果证明了所开发控制方案的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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