A novel error observer-based adaptive output feedback approach for control of uncertain systems

We develop an adaptive output feedback control methodology for nonaffine in control of uncertain systems having full relative degree. Given a smooth reference trajectory, the objective is to design a controller that forces the system measurement to track it with bounded errors. A neural network with linear parameters is introduced as an adaptive signal. A simple linear observer is proposed to generate an error signal for the adaptive laws. Ultimate boundedness is shown through Lyapunov's direct method. Simulations of a nonlinear second-order system illustrate the theoretical results.