Command Filter-Based Adaptive NN Control for MIMO Nonlinear Systems With Full-State Constraints and Actuator Hysteresis

被引：216
作者：
Qiu, Jianbin [1 ,2 ]
Sun, Kangkang [1 ,2 ]
Rudas, Imre J. [3 ]
Gao, Huijun [1 ,2 ]
机构：
[1] Harbin Inst Technol, State Key Lab Robot & Syst, Harbin 150080, Peoples R China
[2] Harbin Inst Technol, Res Inst Intelligent Control & Syst, Harbin 150080, Peoples R China
[3] Obuda Univ, Antal Bejczy Ctr Intelligent Robot, Univ Res Innovat & Serv Ctr, H-1034 Budapest, Hungary
来源：
IEEE TRANSACTIONS ON CYBERNETICS | 2020年 / 50卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Nonlinear systems; MIMO communication; Hysteresis; Artificial neural networks; Actuators; Backstepping; Adaptive systems; Actuator hysteresis; command filter; full state constraints; multi-input and multioutput (MIMO) nonlinear systems; neural network (NN) control; BARRIER LYAPUNOV FUNCTIONS; OUTPUT-FEEDBACK CONTROL; NEURAL-CONTROL; FUZZY-SYSTEMS;
D O I：
10.1109/TCYB.2019.2944761
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This article studies the issue of adaptive neural network (NN) control for strict-feedback multi-input and multioutput (MIMO) nonlinear systems with full-state constraints and actuator hysteresis. Radial basis function NNs (RBFNNs) are introduced to approximate unknown nonlinear functions. The command filter is adopted to solve the issue of "explosion of complexity." By applying a one-to-one nonlinear mapping, the strict-feedback system with full-state constraints is converted into a new pure-feedback system without state constraints, and a novel NN control method is proposed. The stability of the closed-loop system is proved via the Lyapunov stability theory, and the tracking errors converge to small residual sets. The simulation results are given to confirm the validity of the proposed method.
引用
收藏
页码：2905 / 2915
页数：11
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