Adaptive Output Feedback Control of Uncertain Systems using Single Hidden Layer Neural Networks

We consider adaptive output feedback control of uncertain nonlinear systems, in which both the dynamics and the dimension of the regulated plant may be unknown. Only knowledge of relative degree is assumed. 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 necessitates building a state observer. However, finding a good observer for an uncertain nonlinear system is not an obvious task. We argue that it should be sufficient to build an observer for the output tracking error. Ultimate boundedness of the error signals is shown through Lyapunov like stability analysis. The method is illustrated in the design of a controller for a fourth order nonlinear system of relative degree 2 and a high-bandwidth attitude command system for a model R-50 helicopter.