Fuzzy Model Reference Learning Control

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 paper, 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 which observes the plant outputs and adjusts the membership functions of the rules in a direct fuzzy controller so that the overall system behaves like a “reference model”. The effectiveness of this “fuzzy model reference learning controller” (FMRLC) 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 (MIMO) two degree-of-freedom robot manipulator.