Stable indirect adaptive HONN control for a class of non affine SISO nonlinear systems

This paper presents adaptive neural tracking control for a class of uncertain single-input single-output (SISO) non-affine nonlinear systems in general form. To deal with the non-affine appearance of the control variables, the Taylor series expansion is employed to transform the systems into a block-triangular affine form in the neighborhood of the ideal unknown control law. The developed neural control scheme based on HONN avoids singularity problem completely by using projection algorithm, and robustifying control term is used to compensate for approximation errors and disturbances. The global stability was investigated by using Lyapunov theory and the effectiveness of the proposed adaptive control scheme is demonstrated through the simulation of a non-affine nonlinear system.