Adaptive control design using stability analysis and tracking errors dynamics for nonlinear square MIMO systems

This paper investigates adaptive control design for nonlinear square MIMO systems. The control scheme is based on recurrent neural networks emulator and controller with decoupled adaptive rates. Networks' parameters are updated according to an autonomous algorithm inspired from the Real Time Recurrent Learning (RTRL). The contributions of this paper are the determination of Lyapunov sufficient stability conditions for decoupled adaptive rates of the emulator and controller and the development of new adaptation strategies based on the tracking error dynamics and Lyapunov stability analysis to improve the closed loop performances. Efficiency of the proposed controller is illustrated with nonlinear system simulations. An application of the developed approaches to a hot-air blower is presented in order to validate simulations results.