RBF网络干扰补偿的跷跷板系统解耦滑模控制研究

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Matl...

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

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.