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

Abstract EN

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.