Observer-Based Adaptive Neural Control for a Class of Nonlinear Non-affine Systems with Unknown Gain Sign
作者： Mohammad M. Arefi , Mohammad R. Jahed-Motlagh
DOI: 10.3182/20110828-6-IT-1002.03362
关键词: Control theory 、 Adaptive control 、 Nonlinear system 、 Observer (quantum physics) 、 Sign (mathematics) 、 Artificial neural network 、 Approximation error 、 Lyapunov function 、 Mathematics 、 Control theory
摘要: Abstract This paper presents an adaptive neural network output feedback controller for a class of uncertain SISO nonlinear non-affine systems. Since the system states are not required to be available measurement, observer is designed estimate states. Comparing existing results, this method does require priori knowledge about sign control gain direction. To deal with unknown gain, Nussbaum-type function used. By using network, approximated and robustifying term used reduce approximation error compensate effect external disturbance. The stability closed-loop analyzed by Lyapunov method. Theoretical results illustrated through simulation example. Numerical simulations confirm effectiveness proposed
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