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 syst em is designed so that its learning controller has the ability to impr ove the performance of the closed-loop system by generating command in puts to the plant and utilizing feedback information from the plant. I n this brief article, we introduce a learning controller that is devel oped by synthesizing several basic ideas from fuzzy set and control th eory, self-organizing control, and conventional adaptive control. We u tilize a learning mechanism that observes the plant outputs and adjust s the membership functions of the rules in a direct fuzzy controller s o that the overall system behaves like reference model. The effectiven ess of this fuzzy model reference learning controller is illustrated b y showing that it can achieve high performance learning control for a nonlinear time-varying rocket velocity control problem and a multiinpu t multi-output two-degree-of-freedom robot manipulator. (C) 1996 John Wiley and Sons, Inc.