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

This paper presents an adaptive output feedback stabilisation method based on neural networks for nonlinear non-minimum phase systems. The proposed controller comprises a linear, a neuro-adaptive, and an adaptive robustifying parts. The neural network is designed to approximate the matched uncertainties of the system. The inputs of the neural network are the tapped delays of the system inputoutput 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 neural network weights and adaptive gains are obtained using the Lyapunov’s direct method. These adaptation laws employ a linear observer of system dynamics that is realisable. 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 (TORA) model.