Adaptive output feedback control of a class of underactuated systems using neural networks

This paper focus on adaptive output feedback control of a class of underactuated systems using neural networks. Through Lyapunov-like stability analysis, adaptive laws are obtained to drive neural networkpsilas free parameter adjustment and at the same time assure uniformly ultimate boundedness of the error signals. The approach permits to enhance the performance of an available linear controller by adding a neural network adaptive element which partially cancels the nonlinear modeling error and that does not lead to loss of stability. Simulation results, using a simplified mathematical model of a three degrees-of-freedom model helicopter, validate the proposed methodology.