Abstract
A learning controller that is developed by synthesizing several basic ideas from fuzzy set and control theory, self-organizing control, and conventional adaptive control is introduced. A learning mechanism that observes the plant outputs and adjusts the rules in a direct fuzzy controller so that the overall system behaves like a reference model is used. The effectiveness of this fuzzy model reference learning controller is evaluated by comparing its performance to that of a self-organizing controller for a cart and pendulum system. >
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