Adaptive neural network output feedback stabilization of nonlinear non-minimum phase systems

作者：Hoseini S M; Farrokhi M^*; Koshkouei A J

来源：International Journal of Adaptive Control and Signal Processing, 2010, 24(1): 65-82.

DOI：10.1002/acs.1111

• adaptive control
• neural networks
• nonlinear systems
• output feedback
• robust control
• stabilization

This paper presents an adaptive output feedback stabilization method based on neural networks (NNs) for nonlinear non-minimum phase systems. The proposed controller comprises a linear, a neuro-adaptive, and an adaptive robustifying parts. The NN is designed to approximate the matched uncertainties of the system. The inputs of the NN are the tapped delays of the system input-output signals. In addition, an appropriate reference signal is proposed to compensate the unmatched uncertainties inherent in the internal system dynamics. The adaptation laws for the NN weights and adaptive gains are obtained using Lyapunov's direct method. These adaptation laws employ a linear observer of system dynamics that is realizable. The ultimate boundedness of the error signals are analytically shown using Lyapunov's method. The effectiveness of the proposed scheme is shown by applying to a translation oscillator rotational actuator model.

• 出版日期2010-1