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This paper focuses on both the structure uncertainty and parameter uncertainty which have been widely explored in the literature of nonlinear system identification. An integrated analytic framework is proposed for automated neural network structure selection, parameter identification and hysteresis network switching with guaranteed neural identification performance.
In this paper, a novel adaptive neural network control scheme is put forward for a class of SISO nonlinear systems with backlash-like hysteresis, where the hysteresis is modeled by a differential equation in the presence of bounded external disturbances. By using the merit of tangent function and the method of minimal parameterization, the proposed designs need no requirements for the knowledges of...
The high precision of a piezo-electric positioning stage almost depends on whether the designed controller can effectively compensate the inherent hysteresis phenomenon. In this paper, an adaptive output feedback controller based on a radial basis function neural network (RBFNN) is proposed to eliminate the tracking errors caused by the hysteresis behavior. The observer-based RBFNN is used to online...
This paper deals with the tracking problem of a class of uncertain nonlinear systems with ferromagnetic hysteresis nonlinearity, in which the adaptive backstepping control method is presented. The ferromagnetic hysteresis model is approximated using a linear input and a bounded nonlinear disturbance of which the bound is unknown. The designed controller guarantees that the output of the system tracks...
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