Abstract
An adaptive control scheme is presented for systems with unknown hysteresis. In order to handle the case where the hysteresis output is unmeasurale, a novel model is firstly developed to describe the characteristic of hysteresis. This model is motivated by Preisach model but implemented by using neural networks ( NN) . The main advantage is that it is easily used for controller design. Then, the adaptive controller based on the proposed model is presented for a class of SISO nonlinear systems preceded by unknown hysteresis, which is estimated by the proposed model. The laws for model updating and the control laws for the neural adaptive controller are derived from Lyapunov stability theorem, therefore the semiglobal stability of the closed-loop system is guaranteed. At last, the simulation results are illustrated.
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This work was partially supported by National Science Foundation of China ( No. 50265001 ) and Guangxi Science Foundation (No. 0339068).
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Li, C., Tan, Y. Neural model-based adaptive control for systems with unknown Preisach-type hysteresis. J. Control Theory Appl. 2, 51–59 (2004). https://doi.org/10.1007/s11768-004-0023-9
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DOI: https://doi.org/10.1007/s11768-004-0023-9