Predictive Iterative Learning Speed Control With On-Line Identification for Ultrasonic Motor

Abstract

Aiming at the needs of ultrasonic motor motion control, a new two-dimensional (2D) predictive control objective function is proposed. Different from the existing methods, the objective function consists of three terms, including the product of the control quantity and the error of the previous control process. Based on the objective function, the predictive iterative learning control (ILC) law is derived by using the design method of generalized predictive control (GPC) without specifying ILC law form in advance. An on-line identification method for model parameters is given to realize effective identification under a small amount of data circumstances, and therefore, the parameters of controller are adjusted adaptively according to the identification results. The proposed control method is validated both in simulation and experiment. The experimental results show that the proposed predictive iterative learning control strategy can obtain better control effect than GPC, and has more obvious characteristics of iterative learning control. It can maintain the expected performance under the condition of intermittent loading and replacing the motor. It presents strong robustness. INDEX TERMS Ultrasonic motor, iterative learning control, generalized predictive control, on-line identification.

Non-uniform Trajectory Tracking AILC for Nonlinear Time-varying Parameter System with Multiple Unknown Control Directions

“…Iterative learning control can obtain the control effect that conforms to the desired trajectory by continuously using the previous experimental information within a limited time interval. Due to its adaptability and easy to accomplish, it is used in many fields, such as drone [1], intelligent robot [2], multi-agent systems [3], high-speed trains [4], Motor [5] and so on. Nowadays, there are more and more researches on ILC, but most of the literatures stay on the control system with uniform tracking trajectory.…”

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.…”