Direct Adaptive Backstepping Control for a Class of MIMO Non-affine Systems Using Recurrent Neural Networks

This paper proposes a direct adaptive backstepping control scheme for a class of multi-input-multi- output nonlinear uncertain non-affine systems using output recurrent wavelet neural networks (ORWNNs), called DABCORWNN. The proposed ORWNN combines the advantages of wavelet-based neural network, fuzzy neural network (FNN), and output feedback layer. For the tracking of nonlinear non-affine systems with non-triangular form, we first transform it into a strict-feedback-like form. Subsequently, the neural network based backstepping controller is developed. The ideal virtual controllers and actual controller are approximated by ORWNNs. In addition, a robust controller is designed to compensate the approximated error of ORWNNs. Based on the Lyapunov approach, the adaptive laws and stability analysis of closed-loop system is obtained. Finally, simulation results of non-affine double-pendulums system is shown to demonstrate the performance of our approaches.

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