An SPR approach for adaptive output feedback control with neural networks

A direct adaptive output feedback control design procedure is developed for highly uncertain nonlinear systems, that does not rely on state estimation. The approach is also applicable to systems of unknown, but bounded dimension. This includes systems with both parametric uncertainties and unmodelled dynamics. This result is achieved by extending the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input/output data. The network weight adaptation rule is derived from Lyapunov stability analysis, that guarantees boundedness of the NN weights and the system tracking errors. Numerical simulations of an output feedback controlled Van der Pol oscillator, coupled with a linear oscillator, are used to illustrate the practical potential of the theoretical results