RBF网络干扰补偿的跷跷板系统解耦滑模控制研究

doi:10.12068/j.issn.1005-3026.2021.05.011

东北大学学报（自然科学版） ›› 2021, Vol. 42 ›› Issue (5): 679-686.DOI: 10.12068/j.issn.1005-3026.2021.05.011

RBF网络干扰补偿的跷跷板系统解耦滑模控制研究

陆志国，王世雄，林梦磊

(东北大学机械工程学院与自动化学院，辽宁沈阳110819)

修回日期:2020-07-13 接受日期:2020-07-13 发布日期:2021-05-20
通讯作者: 陆志国
作者简介:陆志国(1982-)，男，辽宁锦州人，东北大学教授，博士生导师.
基金资助:
基金项目；(半空) 基金项目.国家重点研发计划项目(2018YFB1304504)；中央高校基本科研业务费专项资金资助项目(N182410007-05).

Study on Decoupling Sliding Mode Control with RBF Network for the Interference Compensation of Seesaw System

LU Zhi-guo， WANG Shi-xiong， LIN Meng-lei

School of Mechanical Engineering & Automation， Northeastern University， Shenyang 110819， China.

Revised:2020-07-13 Accepted:2020-07-13 Published:2021-05-20
Contact: WANG Shi-xiong
About author:-
Supported by:
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摘要/Abstract

摘要： 针对典型的不稳定、高阶次、多变量、强耦合、非线性的跷跷板系统，考虑环境对跷跷板的作用，提出了一种RBF网络干扰补偿解耦滑模控制(RBF-SMC)算法.通过解耦算法对模型进行解耦，并使用RBF神经网络对模型受到的干扰和不确定项自适应逼近补偿，使系统在较小的切换增益下实现较大干扰下的跷跷板平衡控制.在Matlab和Matlab/Adams联合仿真的环境下，对该算法进行了仿真.仿真结果表明，对比传统的SMC算法，在不确定环境下，通过RBF网络对外加干扰、建模误差、模型简化、外部激励、摩擦阻尼等建模不确定性因素进行学习评估，有效地提升了系统抗干扰能力，同时降低了系统的切换增益，并在有限时间内实现了跷跷板的平衡控制.通过仿真实验结果的比较，证明了本文提出算法的有效性与可行性.

关键词: 跷跷板系统;滑模控制;RBF网络;解耦算法;干扰补偿

Abstract: For a typical unstable， high-order， multi-variable， strong-coupling， nonlinear seesaw system， an adaptive decoupling sliding mode control (SMC) algorithm based on RBF network(RBF-SMC) is proposed considering the effect of the environment on the seesaw. The decoupling algorithm is used to decouple the model， and the RBF neural network is used to compensate for the interference and uncertainty of the model， which realizes the seesaw balance control under large interference with small switching gain. The algorithm is simulated in Matlab and Matlab/Adams and the results indicate that compared with the existing SMC methods， using the RBF network in an uncertain environment to learn and evaluate the external interference， modeling error， model simplification， external excitation， friction damping and so on， not only improves the anti-interference ability of the system effectively， but also reduces the switching gain of the system and realizes the balance control of the seesaw in a limited time. Through the comparison of simulation results， the validity and practicality of the proposed algorithm are proved.

Key words: seesaw system; sliding mode control (SMC); RBF network; decoupling algorithm; interference compensation

中图分类号:

TP273+.3

陆志国，王世雄，林梦磊. RBF网络干扰补偿的跷跷板系统解耦滑模控制研究[J]. 东北大学学报（自然科学版）, 2021, 42(5): 679-686.

LU Zhi-guo， WANG Shi-xiong， LIN Meng-lei. Study on Decoupling Sliding Mode Control with RBF Network for the Interference Compensation of Seesaw System[J]. Journal of Northeastern University(Natural Science), 2021, 42(5): 679-686.

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RBF网络干扰补偿的跷跷板系统解耦滑模控制研究

Study on Decoupling Sliding Mode Control with RBF Network for the Interference Compensation of Seesaw System

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