Data-driven model-free direct adaptive generalized predictive control for linear motor based on cSPACE

Data-driven model-free direct adaptive control (DDMFDANPC) approach of linearization of tight format of a class of SISO nonlinear systems based on a generalized predictive control (GPC) is applied to linear motor position control in this paper. The design of controller is based directly on estimate and prediction of pseudo-partial-derivatives (PPD) derived on-line from the input and output information of the motor motion model using a novel parameter estimation algorithm, predicted by approach for multi-degree prediction. Stability, validity and robustness against exogenous disturbance are proved for nonlinear systems with vaguely known dynamics by real experiment research. Based on Weina technology company' cSPACE real time control system, running track of linear motor can be real-time observed by graphical manner, the controller parameters can be on-line modified to achieve real-time effective control.