Adaptive Neural Network Backstepping Control of Fractional-Order Nonlinear Systems With Actuator Faults
Adaptive Neural Network Backstepping Control of Fractional-Order Nonlinear Systems With Actuator Faults
复制标题
具有执行器故障的分数阶非线性系统的自适应神经网络反步控制
DOI：
10.1109/tnnls.2020.2964044
复制
发表时间：
2020-12-01
影响因子：
10.4
通讯作者：
Zhou, Yan
中科院分区：
文献类型：
--
作者：
Liu, Heng;Pan, Yongping;Zhou, Yan
分数阶非线性系统的反步控制需要对某些复杂稳定函数的分数阶导数进行解析计算，随着系统阶数的增加，这一过程变得难以实现。本文的目的是促进具有执行器故障且参数和模式完全未知的fss的自适应神经网络反步控制设计。采用分数阶滤波方法生成命令信号及其分数阶导数，避免了解析分数阶微分的需要。分数阶滤波器产生的补偿跟踪误差可以消除指令信号的近似误差。提出的自适应神经网络命令滤波反步控制（ANNCFBC）方法，结合分数阶自适应律，不仅保证了所有相关变量的有界性，而且保证了跟踪误差和补偿跟踪误差收敛到一个足够小的区域。最后，通过仿真实验验证了所提控制方法的有效性。
Backstepping control for fractional-order nonlinear systems (FONSs) requires the analytic calculation of fractional derivatives of certain complicated stabilizing functions, which becomes prohibitive as the order of the system increases. This article aims to facilitate the adaptive neural network (NN) backstepping control design for FONSs with actuator faults whose parameters and patterns are fully unknown. A fractional filtering approach, which obviates the requirement of analytic fractional differentiation, is used to generate command signals together with their fractional derivatives. Compensated tracking errors that can eliminate approximation errors of command signals are generated by fractional filters. The proposed adaptive NN command filtered backstepping control (ANNCFBC) approach, together with fractional adaptive laws, guarantees not only the boundedness of all involved variables but also the convergence of both the tracking error and the compensated tracking error to a sufficiently small region. Finally, simulation studies are given to indicate the effectiveness of the proposed control method.