Zhang Chen-Liang, Guo Ge
IEEE Trans Cybern. 2023 Dec;53(12):7824-7833. doi: 10.1109/TCYB.2022.3227389. Epub 2023 Nov 29.
This article investigates the prescribed performance control (PPC) problem for a class of nonlinear strict-feedback systems with sensor/actuator faults. A shifting function is introduced to modify the output tracking error generated by the practically measured system state, based on which an improved PPC method is proposed to achieve the convergence of output tracking error to the prescribed region, and this convergence is shown to be independent of the initial tracking condition and insusceptible to sensor/actuator faults. The faults-induced uncertainties together with the nonlinear dynamics are compensated by involving a radial basis function neural network (RBFNN) to make the controller robust adaptive fault-tolerant without prior knowledge of fault coefficients. Via Lyapunov stability analysis, it is proven that all signals in the closed-loop system are semiglobally uniformly ultimately bounded. The effectiveness and superiority of the method are demonstrated by two simulation examples.
本文研究了一类具有传感器/执行器故障的非线性严格反馈系统的规定性能控制（PPC）问题。引入了一个切换函数来修正由实际测量的系统状态产生的输出跟踪误差，在此基础上提出了一种改进的PPC方法，以实现输出跟踪误差收敛到规定区域，并且这种收敛被证明与初始跟踪条件无关，且不受传感器/执行器故障的影响。通过引入径向基函数神经网络（RBFNN）来补偿故障引起的不确定性以及非线性动力学，使得控制器在无需故障系数先验知识的情况下具有鲁棒自适应容错能力。通过李雅普诺夫稳定性分析，证明了闭环系统中的所有信号都是半全局一致最终有界的。两个仿真例子验证了该方法的有效性和优越性。
