Model Reference Adaptive Iterative Learning Speed Control for Ultrasonic Motor - Shi Jingzhuo; Wenwen Huang (IEEE Access, 2020)
In the design process of the controller, the adaptive gain of model reference adaptive control (MRAC) often requires a tradeoff between the adaptive ability, robustness and stability of the control system. The tradeoff of adaptive gain leads to poor control performance and increase design difficulty. Aiming at this problem, the iterative learning idea is introduced into the model reference adaptive control strategy. The control parameter adaptive law based on the parameters of the previous control process is designed. For scalar systems, a new control strategy is constructed, which is the combination of MRAC and iterative learning control (ILC). The adaptive ability of the model reference adaptive controller is improved by using learning ability of ILC. An appropriate composite energy function is designed to prove the uniform convergence of the proposed control strategy and the boundedness of the control quantity. The proposed control strategy is applied to the ultrasonic motor. The effectiveness of the proposed control strategy is verified by experiments and simulations. The controller is designed by using the first-order model that is large different from the actual object. It verifies that the control strategy has strong robustness to model deviation and online time-varying characteristics.