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.