Permanent magnet synchronous motor algorithms based on nonlinear identification generalized predictive and Intelligent fuzzy control system

A control strategy of permanent magnet-oriented field synchronous motor based on intelligent fuzzy control system and generalized predictive control with non-linear identification is proposed to develop the effectiveness of the controlling method of constant magnet-oriented field synchronous motor, the accessor can be split into stabilization control part and intelligent control part. The input of traditional feedback control is used as the stabilization control part, while the feed-forward is incorporated into the intelligent part to compensate for the uncertainties of repetitive load torque and model parameters. The proposed feed forward compensation term uses simple learning rules without any load torque disturbance observer. The additional learning feed forward term does not require information about motor parameters and load torque values, it is insensitive to load torque uncertainty and model parameters, and does not need to identify the system model. With that, the solidness and intermingling confirmation of the proposed control framework reaction is given. The exploratory outcomes demonstrate that the proposed technique has littler speed overshoot list, and the heap torque against aggravation capacity list is improved by over 30%.