Higher-order model-free adaptive control for permanent magnet linear motor

A higher-order model-free adaptive control is presented for improving the permanent magnet linear motor position and velocity tracking performance. The controller is comprised of the adaptive control law and the PPD updating law. The control law gain is not linear but expressed by a nonlinear function with the PPD parameter tuned on-line by the updating law. The control design is model-free and just depends on the I/O data of the system, without requiring any other priori. By introducing higher-order learning law, this method can incorporate more control information obtained in previous sampling time instants, with a result of improving the convergence performance greatly. Simulation results illustrate the validity of the presented method.