An Adaptive Neuro-Fuzzy Controller for Vibration Suppression of Flexible Structuress

被引：6

作者：

Genno, Adam ^{[1
]}

Wang, Wilson ^{[2
]}

机构：

[1] Honda Canada Mfg, Alliston, ON L9R 1A2, Canada

[2] Lakehead Univ, Dept Mech Engn, Thunder Bay, ON P7B 5E1, Canada

来源：

IEEE-ASME TRANSACTIONS ON MECHATRONICS | 2024年 / 29卷 / 02期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Adaptive control; flexible structures; hybrid training; neuro-fuzzy (NF) systems; parameter training; vibration suppression; SLIDING MODE CONTROL; SERVO MECHANISMS; SYSTEM; OPTIMIZATION; TRACKING; COLONY;

D O I：

10.1109/TMECH.2023.3314640

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Neuro-fuzzy (NF) controllers are useful in a wide range of industrial applications due to their adaptive capability by proper training. In this article, an adaptive NF controller is developed to suppress the vibration of flexible structures. A new training method based on the bisection particle swarm optimization (BPSO) algorithm is proposed to optimize the NF controller parameters recursively. To reduce additional vibration induced by controller response to nonlinearities in the steady-state solution space, a fuzzy boundary function is suggested to shape the control signal and reduce the induced vibrations from the control action. The parameters related to output suppression are optimized simultaneously during recursive NF system training. The effectiveness of the adaptive NF control technique and the BPSO training method are validated by a series of experimental tests.

引用

页码：1342 / 1351

页数：10

共 35 条

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