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

Jianbin Qiu, Kangkang Sun, Imre J. Rudas, Huijun Gao

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number8879661
Pages (from-to)2905-2915
Number of pages11
JournalIEEE Transactions on Cybernetics
Volume50
Issue number7
DOIs
Publication statusPublished - Jul 2020

Keywords

  • Actuator hysteresis
  • command filter
  • full state constraints
  • multi-input and multioutput (MIMO) nonlinear systems
  • neural network (NN) control

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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