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A Design Scheme of Adaptive Switching Neural Control with Uncertain Nonlinearity and External Disturbance

  • Lei Yu
  • Junyi Hou
  • Jun Huang
  • Yongju Zhang
  • Wei Zhang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 528)

Abstract

In this paper, we concern with the robust adaptive tracking control problem using neural networks for switched nonlinear systems with uncertain nonlinearity and external disturbance. The hypothesis condition that the sign of control gain is known has been relaxed by the proposed control strategy. RBF neural networks (NNs) are utilized to model the unknown nonlinear functions and a robust adaptive neural tracking control method is recommended to enhance the switching the system robustness. Based on switched multiple Lyapunov function strategy, we have derived the adaptive updated control law and the appropriate switching law. It is shown that the technique proposed is able to guarantee that the resulting closed-loop system is asymptotically stable in the Lyapunov sense such that the system output tracking error performance can be well obtained. The effectiveness of the presented control method is demonstrated by the simulation results.

Keywords

Tracking control Switched nonlinear systems RBF neural networks 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Lei Yu
    • 1
    • 2
    • 3
  • Junyi Hou
    • 1
  • Jun Huang
    • 1
  • Yongju Zhang
    • 4
  • Wei Zhang
    • 4
  1. 1.School of Mechanical and Electric EngineeringSoochow UniversitySuzhouChina
  2. 2.Collaborative Innovation Center of Industrial Energy-Saving and Power Quality Control, Anhui UniversityHefeiChina
  3. 3.Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministryof EducationNanjing University of Science and TechnologyNanjingChina
  4. 4.Special Equipment Safety Supervision Inspection Institute of Jiangsu Province, Wujiang BranchWujiangChina

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