Strong Robust and Optimal Chaos Control for Permanent Magnet Linear Synchronous Motor

Abstract

To effectively solve the chaotic phenomenon problem in permanent magnet linear synchronous motor (PMLSM), this paper presents a novel control scheme combining radial basis function neural network (RBFNN), adaptive backstepping method, and particle swarm optimization (PSO) algorithm. By applying a feedback decoupling controller, a decoupled chaotic model of the PMLSM is constituted. In addition, in order to enhance the robustness of the system, the RBFNN is utilized to identify the uncertainties in PMLSM and the convergence of the overall closed-loop system, including unknown parameters is guaranteed based on the adaptive backstepping method. Moreover, the PSO is applied to promote the dynamic performance of the control system. The simulation results demonstrate the existence of chaotic phenomenon in the PMLSM. Besides, PSO-RBFNN controller that has strong robustness can make the motor out of chaos rapidly and smoothly, and identify the unknown parameters quickly and accurately.

Keywords

• Chaos control
• Lyapunov stability
• permanent magnet linear synchronous machine
• particle swarm optimization
• radial basis function neural network