反推
分数阶微积分
控制理论(社会学)
执行机构
非线性系统
跟踪误差
人工神经网络
计算机科学
趋同(经济学)
自适应控制
跟踪(教育)
数学
控制(管理)
应用数学
人工智能
心理学
教育学
物理
量子力学
经济
经济增长
作者
Heng Liu,Yongping Pan,Jinde Cao,Hongxing Wang,Yan Zhou
出处
期刊:IEEE transactions on neural networks and learning systems
[Institute of Electrical and Electronics Engineers]
日期:2020-12-01
卷期号:31 (12): 5166-5177
被引量:119
标识
DOI:10.1109/tnnls.2020.2964044
摘要
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.
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