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

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.