Adaptive Control of SISO Nonlinear System Using Data-driven SCN Model
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.
Adaptive controlstochastic configuration network(SCN)supervision mechanismunmodeled dynam-icdata-driven model
万方数据
维普
