Improved Indirect Iterative Learning MIT Control Method for Ultrasonic Motor

Abstract

MIT control strategy is a kind of model reference adaptive control method with the simplest structure. The reason why the structure is simple is that only the gain of the controlled object is adaptively adjusted. Because only the gain is adjusted, the ability of MIT control strategy to change the characteristics of the controlled object is limited. This also limits its application. In this paper, the simple idea of iterative learning control is introduced into MIT controller to increase the controller's ability to adjust the characteristics of the controlled object, and make it suitable for complex control objects. In the proposed control method, the output of the iterative learning controller is used to adjust the adaptive law of the MIT controller. The proposed control method is applied to the speed control system of ultrasonic motor. Experiments show that although only the gain of MIT controller can be adjusted, the learning process based on memory increases the degree of control freedom. Therefore, the dynamic characteristics of the system can be greatly changed, and the control performance can be significantly improved. Moreover, the proposed method only needs to add a simple P-type iterative learning controller to the MIT controller, and the increase of online calculation amount is small. INDEX TERMS Ultrasonic motor, iterative learning control (ILC), MIT control.

Robust Speed Control of Ultrasonic Motors Based on Deep Reinforcement Learning of a Lyapunov Function

“…Furthermore, in [11], neural networks were combined with fuzzy logic to get a controller with neural networks flexibility, and fuzzy logic expertise. In [12] and [13], iterative learning and model predictive controllers could adapt to changes in motor behavior by online learning. Despite their clear advantage over linear controllers, the proposed nonlinear controllers are still lacking in a few aspects.…”

“…In addition to the suboptimality of the interpolation region, extrapolation might result in instability. On the other hand, online iterative controllers in [12] and [13] can realize a zero steady error; however, they require running multiple iterations for control output optimization. This would be a feasible solution for constant trajectory tracking but would underperform (higher errors) for continuously changing unknown trajectories.…”

“…Similarly, proposed controllers in the literature (linear controllers and controllers with inherently linear structure (i.e. Fuzzy-PID [10], MIT Control [13])) limit the frequency control range to avoid discontinuity and hysteresis in the speed response. Such constraints can produce reliable controllers at the expense of limiting the motor output.…”