Dynamically focused fuzzy learning control

Abstract

A possesses the capability to improve its performance over time by interaction with its environment. A control system is designed so that its 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. 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 process may include: (i) a natural tendency for the human to focus their by paying particular attention to the current operating conditions of the system since these may be most relevant to determining how to enhance performance; (ii) after how to control the plant for some operating condition, if the operating conditions change, then the best way to control the system may have to be re-learned; and (iii) a human with a significant amount of experience at controlling the system in one operating region should not forget this experience if the operating condition changes. To mimic these types of human behavior the authors introduce two strategies that can be used to focus a controller onto the current operating region of the system. The authors show how the subsequent dynamically focused learning (DFL) can be used to enhance the performance of the model reference (FMRLC) and furthermore the authors perform comparative analysis with a conventional adaptive control technique. A magnetic ball suspension system is used throughout the paper to perform the comparative analyses, and to illustrate the concept of focused fuzzy control.