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Title: Chaos control of the brushless direct current motor using adaptive dynamic surface control based on neural network with the minimum weights

This paper investigates chaos control for the brushless DC motor (BLDCM) system by adaptive dynamic surface approach based on neural network with the minimum weights. The BLDCM system contains parameter perturbation, chaotic behavior, and uncertainty. With the help of radial basis function (RBF) neural network to approximate the unknown nonlinear functions, the adaptive law is established to overcome uncertainty of the control gain. By introducing the RBF neural network and adaptive technology into the dynamic surface control design, a robust chaos control scheme is developed. It is proved that the proposed control approach can guarantee that all signals in the closed-loop system are globally uniformly bounded, and the tracking error converges to a small neighborhood of the origin. Simulation results are provided to show that the proposed approach works well in suppressing chaos and parameter perturbation.
Authors:
 [1] ;  [2] ;  [3] ;  [1]
  1. School of Automation, Chongqing University, Chongqing 400044 (China)
  2. (China)
  3. Department of Mechanical Engineering, Chongqing Aerospace Polytechnic, Chongqing, 400021 (China)
Publication Date:
OSTI Identifier:
22483214
Resource Type:
Journal Article
Resource Relation:
Journal Name: Chaos (Woodbury, N. Y.); Journal Volume: 25; Journal Issue: 7; Other Information: (c) 2015 AIP Publishing LLC; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; APPROXIMATIONS; CHAOS THEORY; CONTROL; DIRECT CURRENT; FUNCTIONS; MOTORS; NEURAL NETWORKS; NONLINEAR PROBLEMS; PERTURBATION THEORY; SIGNALS; SIMULATION