Analysis and design of an optimal learning control scheme for industrial robots: A discrete system approach

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Abstract
Analysis and design of a discrete control system that can improve its performance in the course of operation is described in this paper. Such a system is called "discrete learning systems." A new discrete learning algorithm for controlling electro-mechanical systems such as industrial robots is proposed. The algorithm utilizes the error of state variables of the system, which includes positional and velocity error. A condition of algorithmic convergence is obtained. Advantages of a discrete system approach over an analog approach are discussed. Similarity between a learning controller and an state observer is also discussed. In most cases the learning control law is not, uniquely obtained from the condition of algorithmic convergence. We apply system optimization schemes to learning control and identify the learning control law in several ways. We propose better forms for the gain which is defined uniquely. We also show that the gain obtained is a generalized inverse solution to the optimal learning control problem. A discrete learning control scheme based on the proposed algorithm is effectively applied to control an industrial robot.

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