Periodic Learning Control for a Class of Nonlinear Systems

In this paper, periodic learning control is presented to deal with the problem of trajectory tracking of a class of nonlinear systems with time-varying parametric uncertainties. The system undertaken is assumed to be in the form of strict-feedback and the unknown parameters are periodic with known periodicity. Backstepping approach is utilized for the controller design and the developed learning law is a differential-difference learning law, which avoids the difficulty in obtaining derivatives of parameter estimations. This design has the advantage of ensuring tracking, while the boundness of all the signal in the closed loop is guaranteed.