Theory of P-type learning control with implication for the robot manipulator

Abstract

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