In this paper, a data-based fault tolerant control ( FTC ) scheme is investigated for unknown continuous-time ( CT ) affine nonlinear systems with actuator faults. First, a neural network ( NN ) identifier based on particle swarm optimization ( PSO ) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network ( PSOCNN ) is employed to solve the Hamilton-Jacobi-Bellman equation ( HJBE ) more efficiently. Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method.

中文翻译：

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基于粒子群优化神经网络的仿射非线性系统基于数据的容错控制

本文针对具有执行器故障的未知连续时间（仿射）仿射非线性系统，研究了一种基于数据的容错控制（FTC）方案。首先，构造基于粒子群优化（PSO）的神经网络（NN）标识符来对未知系统动力学进行建模。通过利用估计的系统状态，粒子群优化的批评者神经网络（PSOCNN）被用来更有效地求解Hamilton-Jacobi-Bellman方程（HJBE）。然后，提出了一种由NN标识符和故障补偿器组成的基于数据的FTC方案，以实现执行器的容错能力。Lyapunov稳定性定理可确保执行器故障时闭环系统的稳定性。最后，提供仿真以证明所开发方法的有效性。