The model-free direct adaptive generalized predictive control approach of permanent magnet linear motor

In this paper, a model-free direct adaptive nonlinear predictive control (MFDANPC) algorithm of linearization of tight format of a class of SISO based on a generalized predictive control (GPC) is applied to permanent magnet linear motor speed and position control. 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 the simulation examples and real experiments.