DYNAMICALLY FOCUSED FUZZY LEARNING CONTROL

A ''learning system'' possesses the capability to improve its performa nce over time by interacting 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 co mmand inputs to the plant and utilizing feedback information from the plant. Learning controllers are often designed to mimic the manner in which a human in the control loop would learn how to control a system while it operates. Some characteristics of this human learning process may include: (i) a natural tendency for the human to focus their lear ning by paying particular attention to the current operating condition s of the system since these may be most relevant to determining how to enhance performance; (ii) after learning how to control the plant for some operating condition, if the operating conditions change, then th e best way to control the system may have to be relearned; and (iii) a human with a significant amount of experience at controlling the syst em in one operating region should not forget this experience if the op erating condition changes. To mimic these types of human learning beha vior, we introduce three strategies that can be used to dynamically fo cus a learning controller onto the current operating region of the sys tem. We show how the subsequent ''dynamically focused learning'' (DFL) can be used to enhance the performance of the ''fuzzy model reference learning controller'' (FMRLC) [1]-[5] and furthermore we perform comp arative analysis with a conventional adaptive control technique. A mag netic bah suspension system is used throughout the paper to perform th e comparative analyses, and to illustrate the concept of dynamically f ocused fuzzy learning control.