A NEW ROBUST ADAPTIVE CONTROL SCHEME FOR NON-AFFINE NONLINEAR SYSTEMS BASED ON SHLNN DISTURBANCE OBSERVER

A novel adaptive online learning control scheme called single hidden layer neural networks disturbance observer (SHLNNDO) is developed for a class of uncertain non-affine nonlinear systems. In this paper, the term ”disturbance” refers to the combination of model uncertainties and external disturbances. A general approach is provided for using SHLNNDO to enhance disturbance attenuation and robustness of current linear or nonlinear control methods. By Lyapunov’s direct method, a rigorous poof shows that the SHLNNDO can approximate the effects of the disturbances arbitrarily closely. As a demonstration of the application of the approach, a new robust adaptive feedback linearization control (RAFLC) algorithm is proposed by integrating the existing feedback linearization control (FLC) method with the SHLNNDO technique. Conditions are derived which guarantee ultimate boundedness of all the errors in the combined system. Excellent disturbance attenuation ability and strong robustness of the proposed RAFLC method are shown by a numerical example.