Observer-Based Sampled-Data Model-Free Adaptive Control
This paper proposes an observer based sampled-data model-free adaptive control method,which extends the model-free adaptive control from discrete-time systems to contin-uous-time systems.A dynamic linearization method is proposed to transform a class of high-order nonlinear affine continuous-time systems into a nonlinear affine input-output data mod-el based on sampled data.The uncertainty and unknown nonlinearity of the system are transformed into unknown time-varying parameter vectors and nonlinear residual terms,which are estimated and compensated by the designed parameter adaptive update law and ex-tended state observer,respectively.Therefore,a new adaptive control law for sampled data is proposed,which includes the sampling period and more input and output information of past moments,and thus can improve the control performance of the system.Simulation re-sults verify the effectiveness of the proposed method.
continuous-time systemsmodel-free adaptive controlsampled-data controlex-tended state observer
万方数据
维普
