Neural-networked adaptive tracking control for switched nonlinear pure-feedback systems under arbitrary switching
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[1] Nanjing University of Aeronautics and Astronautics
Nanjing University of Aeronautics and Astronautics
China
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[2] University of Adelaide
University of Adelaide
Australia
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- Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Vol. 61, 2015, págs. 119-125
- Idioma: inglés
- Texto completo no disponible (Saber más ...)
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Resumen
This paper deals with the problem of adaptive tracking control for a class of switched uncertain nonlinear systems in pure-feedback form under arbitrary switching. Based on command filtered backstepping design and common Lyapunov function method, a robust adaptive neural-networked control scheme is proposed to guarantee that the resulting closed-loop system is asymptotically bounded with tracking error converging to a neighborhood of the origin. A universal formula for constructing common neural-networked stabilizing function and controller is designed. Differing from the existing results in the literature, the developed new design scheme only requires desired trajectory and common stabilizing functions/virtual control signals instead of them and their first derivatives at each step in backstepping design procedures, and does not need a priori knowledge of the signs of control gain functions. Simulation results illustrate the effectiveness of the proposed techniques.
