High performance controller for interior permanent magnet synchronous motor drive using artificial intelligence method

This paper develops a high performance pi based controller for an interior permanent magnetsynchronous motor (ipmsm) drive. an artificial neural network is used for on-line tuning of the picontroller. the genetic algorithm (ga) has been used in this work in order to obtain the optimized valuesof the controller parameters for precise speed control for different operating conditions over a wide speedrange. in this paper, an integral anti-windup (aw) strategy for the pi speed controller is also utilized tosuppress the undesired side effect known as integrator windup when large set-point changes are made.the optimal behavior of the drive can be achieved by considering two control strategies: maximumtorque per ampere (mtpa) and flux-weakening (fw).in developing the proposed controller, the pi controller parameters and, also, the anti-windup strategyare optimized by ga for some operating conditions, in a closed loop vector control scheme. by obtainingthe parameters at a number of points in the possible operating region, a look-up table approach has beencompleted. then, an ann is trained by this look-up table. ultimately, the well-trained ann is utilizedfor on-line tuning of the controller parameters to ensure optimum drive performance under differentdisturbances and operating conditions.