Direct Model Reference Learning and Repetitive Control

Abstract

Abstract Iterative learning and repetitive controllers learn to improve their performance when repeatedly executing a task. The learning process can be based on many different convergence concepts. However, the similarity of objective with adaptive control suggest the use of adaptive concepts. Here direct model reference adaptive control ideas are converted to produce discrete-time direct model reference learning control and direct model reference repetitive control laws with guaranteed convergence to zero tracking error. These control laws are used to modify the command going into a feedback control system, converging on whatever command is necessary to produce the desired output. The original adaptive control concepts used are for linear time invariant systems without deterministic disturbances, but the learning and repetitive control laws developed handle time varying systems with repetitive disturbances. Such general models are important since they can represent the behavior of nonlinear systems linea...