Abstract:
This paper focuses on backstepping-based adaptive neural control for switched nonlinear systems in nonstrict-feedback form. A structural characteristic of radial basis fu...Show MoreMetadata
Abstract:
This paper focuses on backstepping-based adaptive neural control for switched nonlinear systems in nonstrict-feedback form. A structural characteristic of radial basis function neural networks is first developed. With this structural characteristic, adaptive neural backstepping has been extended to the switched nonlinear systems with nonstrict-feedback structure. By using a common Lyapunov function method, an adaptive neural controller is constructed by backstepping technique. It is shown that under the action of the suggested controller, all the closed-loop signals are bounded and meanwhile the system output follows the desired reference signal well. Finally, a numerical simulation example is used to illustrate the effectiveness of our results.
Published in: IEEE Transactions on Systems, Man, and Cybernetics: Systems ( Volume: 47, Issue: 7, July 2017)
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