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Nonlinear mapping-based feedback technique of dynamic surface control for the chaotic PMSM using neural approximation and parameter identification

Nonlinear mapping-based feedback technique of dynamic surface control for the chaotic PMSM using neural approximation and parameter identification

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This study presents a novel non-linear mapping-based feedback technique for controlling chaotic permanent magnet synchronous motor (PMSM) using dynamic surface control (DSC), neural approximation and parameter identification. Neural networks are utilised to online approximating the unknown system dynamics, adaptive parameter identification is designed to estimate the unknown parameter, and DSC technique circumvents the problem of ‘explosion of complexity’ in the traditional backstepping methodology. The major feature of the non-linear mapping-based feedback technique lies in that the merits of high-gain and low-gain control are synthesised by virtue of a novel non-linear continuous differentiable mapping feedback function, and a novel non-quadratic Lyapunov function is used to analyse the closed-loop system stability caused by the compound function of non-linear feedback. Finally, unprejudiced comparative results are given to demonstrate the effectiveness and advantages of the proposed control scheme.

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