Robustness and Convergence of P-type Learning Control

Abstract

The robustness and convergence of P-type learning control algorithms for a class of time-varying, nonlinear systems to state disturbances, measurement noise at the output, and reinitialization errors at each iteration is studied extensively. We present the uniform boundedness of the system states with respect to the existence of errors of initialization, measurement noises and fluctuations of system dynamics. Furthermore, the system output converges uniformly to the desired one whenever all disturbances tend to zero. Moreover, implication of our results to robot manipulator, and linear systems are presented.