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 …

We consider the adaptive output feedback control of nonlinear systems. Given a smooth
reference trajectory, the problem is to design a controller that forces the system
measurement to track it with bounded errors. The classical approach requires building a
state observer. However, finding a good observer for a highly nonlinear and uncertain plant
is not an obvious task. We argue that it should be sufficient to build an observer for the
output tracking error. The uniform ultimate boundedness of error signals is shown through a …