Affiliation:
1. Department of Mechanical and Electrical Engineering, Shandong University of Science and Technology, Tai’an 271019, China
Abstract
An adaptive high-order neural network (HONN) control strategy is proposed for a hysteresis motor driving servo system with the Bouc-Wen model. To simplify control design, the model is rewritten as a canonical state space form firstly through coordinate transformation. Then, a high-gain state observer (HGSO) is proposed to estimate the unknown transformed state. Afterward, a filter for the tracking errors is adopted which converts the vector error e into a scalar error s. Finally, an adaptive HONN controller is presented, and a Lyapunov function candidate guarantees that all the closed-loop signals are uniformly ultimately bounded (UUB). Simulations verified the effectiveness of the proposed neural network adaptive control strategy for the hysteresis servo motor system.
Funder
National Natural Science Foundation of China
Subject
Multidisciplinary,General Computer Science
Cited by
8 articles.
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