Dynamically focused fuzzy learning control

A "learning system" possesses the capability to improve its performance 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 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 learning process may include: (i) a natural tendency for the human to focus their learning 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 learning 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 learning behavior, we introduce three strategies that can be used to dynamically focus a learning controller onto the current operating region of the system. We show how the subsequent "dynamically focused learning" (DFL) can be used to enhance the performance of the "fuzzy model reference learning controller" (FMRLC) and furthermore we 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 dynamically focused fuzzy learning control.