Robust adaptive neural control for a class of perturbed strict feedback nonlinear systems

This paper presents a robust adaptive neural control approach for a class of perturbed strict feedback nonlinear with both completely unknown virtual control coefficients and unknown nonlinearities. The unknown nonlinearities comprise two types of nonlinear functions: one naturally satisfies the "triangularity condition" and can be approximated by linearly parameterized neural networks; while the other is assumed to be partially known and consists of parametric uncertainties and known "bounding functions". It has been proven that the proposed robust adaptive scheme can guarantee the uniform ultimate boundedness of the closed-loop system signals. Simulation studies show the effectiveness of the proposed approach.