For a class of single-input single-output(SISO)nonlinear discrete dynamical systems which are difficult to establish an accurate model,a novel adaptive control method is proposed based on data-driven model.In the pro-posed approach,stochastic configuration network(SCN)is first employed to build the data-driven nonlinear system model,which adopts direct link and enhancement nodes to approximate the low-order linear and the high-order nonlinear parts(unmodeled dynamics)of system,respectively.Besides,this paper employed an incremental learn-ing algorithm and supervision mechanism to optimize the model structure and model parameters synchronously,which guarantee the universal approximation property of the data-driven model,solving the problems of low model-ing accuracy and unguaranteed model convergence existing in traditional adaptive control with alternate identifica-tion algorithm.Then,the direct link and enhancement nodes are used to design the linear controller and virtual un-modeled dynamics compensator respectively.A new adaptive control approach based on SCN data-driven model is established,and the stability and convergence of the proposed control method are proved.Finally,simulation com-parisons between our proposed method and the classic adaptive control method with alternate identification al-gorithm are made,showing the effectiveness of the proposed method.
关键词
自适应控制/随机配置网络/监督机制/未建模动态/数据驱动模型
Key words
Adaptive control/stochastic configuration network(SCN)/supervision mechanism/unmodeled dynam-ic/data-driven model
维普
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