Adaptive Repetitive Control of Nonlinearly Parameterized Systems Based on Fuzzy Basis Function Networks

Abstract:

In this note, an adaptive repetitive control scheme is presented for a class of nonlinearly parameterized systems based on fuzzy basis function networks (FBFNs). The parameters of the fuzzy rules are tuned with adaptive schemes. The adaptive repetitive control law is derived by using Lyapunov synthesis method to guarantee the closed-loop stability and the tracking performance. By means of FBFNs, the controller singularity problem is solved as it avoids the nonlinear parameterization from entering into the adaptive control and repetitive control. The proposed approach has the added advantage that it does not require an exact structure of the system dynamics. The proposed controller is applied to control a model of permanent-magnet linear synchronous motor (PMLSM) subject to significant disturbances and parameter uncertainties. The simulation results demonstrate the effectiveness of the proposed method in terms of significant reduction in chattering while maintaining asymptotic convergence.

• Periodical: Advanced Materials Research (Volumes 472-475)
• Pages: 1045-1053
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.472-475.1045
• Online since: February 2012
• Authors: Wen Li Sun, Hong Cai, Fu Zhao
• Keywords: Adaptive Control, FBFNS, Nonlinear Parameterization, PMLSM, Repetitive Control

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