Neural network adaptive control for a class of nonlinear systems with unknown-bound unstructured uncertainties

Abstract

This paper presents a neural network adaptive control scheme for the nonlinear systems in strict-feedback form, where the unstructured uncertainties are assumed to be unknown, though they still satisfy certain growth conditions characterized by 'bounding functions' composed of known functions multiplied by unknown constants. All adaptation laws for these unknown bounds are derived from Lyapunov-based method as well as the adaptation laws for the networks' weights estimations. In addition, the unknown control gain functions are not approximated directly by neural networks. Therefore, we can avoid the possible controller singularity problems. Under a certain relaxed assumptions on the control gain functions, proposed control scheme can guarantee that all the signals in the closed-loop system are uniformly ultimately bounded (UUB). Simulation studies are included to illustrate the effectiveness of the proposed scheme, and some practical features of the control laws are also discussed.