Learning unknown functions in cascaded nonlinear systems

In this paper, the problem of learning unknown time functions in cascaded nonlinear systems is studied. The objective is to find an iterative learning control under which nonlinear systems are globally and asymptotically stabilized and the time functions contained in system dynamics are learned. By utilizing a new differential-difference learning law, a learning control is designed to yield both asymptotic stability of the state and asymptotic convergence of the learning error. The design is carried out by applying the backward recursive method