Fuzzy Broad Learning Adaptive Control for Voice Coil Motor Drivers

被引：10
作者：
Hsu, Chun-Fei [1 ]
Chen, Bo-Rui [2 ]
Wu, Bing-Fei [2 ]
机构：
[1] Tamkang Univ, Dept Elect Engn, New Taipei, Taiwan
[2] Natl Yang Ming Chiao Tung Univ, Inst Elect & Control Engn, Dept Elect & Comp Engn, Hsinchu, Taiwan
来源：
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS | 2022年 / 24卷 / 03期
关键词：
VCM driver; Broad-learning system; Online parameter learning; Stability analysis; SLIDING-MODE CONTROL; NEURAL-NETWORK; SYSTEM; IDENTIFICATION; DESIGN;
D O I：
10.1007/s40815-021-01227-2
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The position of a voice coil motor (VCM) driver is difficult to control in a stable and highly precise manner. To address these challenges, this study proposes a fuzzy broad learning adaptive control (FBLAC) system consisting of a fuzzy broad controller and a robust controller. The fuzzy broad controller uses a fuzzy broad-learning system (FBLS) to approximate an ideal controller online, and the robust controller is designed to keep the system stable. The gradient descent method and the chain rule are applied to adjust all the FBLS parameters online to increase its approximation and learning capacities. Furthermore, the experimental results demonstrate that the proposed FBLAC system has good tracking performance and uncertainty rejection properties. The main contributions of this study are as follows: (1) An FBLS with a simple structure and full-tuned parameter learning laws to improve its learning ability was investigated. (2) Stability analysis of the closed-loop FBLAC system was proved based on the gradient descent method and the Lyapunov stability theorem. (3) Several experimental evaluations and analyses were conducted to demonstrate the effectiveness of the proposed FBLAC method.
引用
收藏
页码：1696 / 1707
页数：12
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