Tracking periodic signals for a class of uncertain nonlinear systems

In this paper, a conditional learning control (CLC) is proposed to track periodic signals for a class of nonlinear systems with unknown dynamics. The main advantage of the CLC over the conventional iterative learning control is that monotonic convergence of the control sequence in the iteration domain is achieved, as the CLC ensures the learning is based on the steady-state output, and hence the effect of the vanishing and unknown transient output is minimized. Following this result, the convergence of the tracking errors is obtained. Further, the optimal setting of the learning gains can be obtained in a min-max sense. A simulation example is presented to illustrate the performance and implementation of the CLC. Copyright (C) 2009 John Wiley & Sons, Ltd.