Adaptive Dynamic Surface Inverse Output Feedback Control for a Class of Hysteretic Systems

In this paper, an robust neural adaptive output-feedback inverse control scheme for a class of hysteretic nonlinear systems is proposed. Firstly, by designing a high-gain observer to estimate the states of the system and cope with the uncertainties of the system, only the output of the control system is required to be measurable. Secondly, the nonlinear function in the systems can totally unknown due to the utilization of the neural networks approximator. Finally, the arbitrarily small \(\mathcal {L}_{\infty }\) norm of the tracking error is achieve by adjusting the initial conditions of the unknown parameters.

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