Repetitive learning control for a class of partially linearizable uncertain nonlinear systems

The repetitive learning control for a class of partially feedback linearizable systems is considered. The signal to be tracked is a general periodic function with known period. The considered system, especially, the unlinearizable subsystem of it, contains unknown functions. Only the desired state variables of the linearizable subsystem can be used in control law design. First, some system constants related to the desired system and error system are presented. Then based on these system constants, a state feedback repetitive control algorithm is proposed, which guarantees that all the signals in the closed-loop systems are bounded and the tracking error converges to zero asymptotically. A simulation example is presented to show the utility of the proposed control algorithm.