Improved Indirect Iterative Learning MRAC Strategy for Ultrasonic Motor

As a kind of model-based control strategy, the control performance of model reference adaptive control (MRAC) system is directly determined by the precision of the object's model. But in practical applications, it is difficult to obtain accurate mathematical models of many objects. Even if the model can be obtained, the order of the model will be relatively high. If a low-order model can be used to design a low-complexity MRAC controller and maintain good control performance, it will greatly expand the application of MRAC strategy. Iterative learning control (ILC) method is used to improve the robustness of MRAC to model deviation in this paper. A simple iterative learning controller is designed to adjust the adaptive law of feed-forward gain in MRAC system. The proposed method is applied to the speed control of ultrasonic motor. Experimental results show that even if the MRAC controller is designed using the low-order model, the good control performance that meets expectations can still be obtained through iterative learning. Moreover, the proposed control method has a small amount of calculation, and is suitable for industrial applications.