Abstract
A learning system possesses the capability to improve its performance over time by interaction with its environment. A learning control system is designed so that its learning controller has the ability to improve the performance of the closed-loop system by generating command inputs to the plant and utilizing feedback information from the plant. In this brief article, we introduce a learning controller that is developed by synthesizing several basic ideas from fuzzy set and control theory, self-organizing control, and conventional adaptive control. We utilize a learning mechanism that observes the plant outputs and adjusts the membership functions of the rules in a direct fuzzy controller so that the overall system behaves like reference model. The effectiveness of this fuzzy model reference learning controller is illustrated by showing that it can achieve high performance learning control for a nonlinear time-varying rocket velocity control problem and a multi-input multi-output two-degree-of-freedom robot manipulator.
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