Adaptive stabilization of non-minimum phase nonlinear systems using neural networks

This paper, presents a direct adaptive control design method for uncertain nonlinear non-minimum phase systems. First, an appropriate reference signal is designed such that the internal dynamic subsystem is input-to-state practical stable. Then an output feedback control, which does not rely on the state estimation, is designed such that the output of system asymptotically tracks this reference signal. This controller is comprised of a dynamic linear controller, an adaptive neural network and a discontinuous robustifying term. Stability of the overall system is guaranteed using the small gain theorem. The effectiveness of the proposed scheme is shown in simulations using a nonminimum phase non-affine system.