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

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.

Echo State Network for Extended State Observer and Sliding Mode Control of Vehicle Drive Motor with Unknown Hysteresis Nonlinearity

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“…At an earlier time, there was an unmodeled nonlinear problem in the above system, which greatly limited the application of this technology. To solve the above problems, fuzzy logic systems (FLSs) and neural networks (NNs) were applied extensively to approximate unknown nonlinear function in [3][4][5][6][7]. However, the characteristic of the backstepping method is a class of recursive design procedures coupled with Lyapunov function candidates; hence, the repeated differentiation of virtual controller leads to the complexity explosion problem.…”

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