Model Reference Adaptive Iterative Learning Speed Control for Ultrasonic Motor

Abstract

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.

A PID Control Algorithm With Adaptive Tuning Using Continuous Artificial Hydrocarbon Networks for a Two-Tank System

“…Segment separation is defined by (4), which suggests that, given an input vector X ∈ R n and a molecule center µ m ∈ R n , X ∈ Σ m holds if µ m is the molecule center that is spatially closest to X. This implies that the distances between X and the centers of the other molecules must be greater, a condition that can be expressed in the Boolean form using a series of logical conjunctions, as shown in (9). where m ̸ = j and r i is the distance between the vectors X and µ i .…”

“…In direct control methods, the controller parameters are adjusted directly from the system data, whereas in indirect control methods, they are adjusted online using recursive parameter estimation [8]. An example of indirect adaptive control is the reference model based control (MRAC), which has been used in many control systems because of its adaptability and stability guaranteed by process design based on the Lyapunov function [9].…”

Multi‐input enhanced model reference adaptive control strategies and their application to space robotic manipulators

“…An established theoretical framework supports this control approach, which, over the past decades, has been shown to be a viable solution to control engineering plants with unknown parameters. 1,2 Nowadays, MRAC research focuses on improving the closed-loop performance of the original algorithms (e.g., by combining MRAC with other control techniques such as sliding mode control, 3 iterative learning control 4 or fuzzy algorithms, 5 just to name a few) and further extend the MRAC theory, for example, to fractional order systems, 6 switching control systems, 7,8 and piecewise affine systems. [9][10][11] To improve the closed-loop tracking performance in presence of plant parameter mismatches, unmodeled plant dynamics, rapid varying disturbances, and unknown system nonlinearities, in Reference 12 the MRAC strategy was augmented by an adaptive integral control action and an adaptive switching control action.…”

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“…Additional benefits of MRAC include its capacity to operate a system that is subject to ambient or parameter changes [6]. MRAC has been successfully used in a number of applications, including temperature [7,8], pH [9], speed control of synchronous motor drive [10], speed control of ultrasonic motor [11], aortic pressure regulation [12], control of rotorcraft [13], nuclear reactor power control [14], etc.…”