Iterative learning control of perfect tracking for the variable trajectory driven by DFSM

The practical tracking and perfect tracking for the variable trajectories driven by a deterministic finite state machine (DFSM) are addressed in this paper. The DFSM is repressed by a linear state equation with linear-inequalities, and a state feedback controller is designed to stabilize the DFSM at an equilibrium set. Then, the variable trajectories are driven by this DFSM, a novel iterative learning control (ILC) law and an initial state learning law are proposed to track the variable trajectories, and the sufficient conditions of practical tracking and perfect tracking are derived. Finally, a numerical simulation is given to illustrate the effectiveness of this method.