Model reference learning control for non-linear systems

In this paper, we propose a model reference learning control (MRLC) for nonlinear SISO plants where trials can do repeatedly from a same initial condition. It is step by step accomplished through off-line learning trials, not learning control input, but estimating optimal values of adjustable functions in feedback controller using input and output data. The only a priori information needed is the relative degree between input and output. Viability of the proposed control system is shown by a simple numerical simulation