Adaptive Neural Control for Hysteresis Motor Driving Servo System with Bouc-Wen Model

Xuehui Gao

Complexity, 2018, vol. 2018, 1-9

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 into a scalar error . 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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:9765861

DOI: 10.1155/2018/9765861

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