A Learning-Based Set-point Tracking Control Scheme for a Class of Nonlinear Cascaded Systems

In this paper, we propose a learning-based set-point tracking control scheme for a class of nonlinear cascaded systems. The additive periodic disturbance is allowed in the input channel. By means of the notions of input-to-state stability (ISS) and ISS-Lyapunov functions, we characterize the cascaded dynamic uncertainty and develop the changing supply rates technique with uncertain supply rates. A new adaptive set-point tracking controller, based on an iterative learning control strategy, is proposed to asymptotically regulate the system to a desired reference while compensating for periodic, exogenous disturbances. Numerical simulation results are presented to illustrate the performance of the proposed controller.