There are two main drawbacks in fuzzy control: 1) the design of fuzzy controllers is usually performed in an ad hoc manner where it is often difficult to choose some of the controller parameters; and 2) the fuzzy controller constructed for the nominal plant may later perform inadequately if significant and unpredictable plant parameter variations occur. In this paper we illustrate these two problems on a two-link flexible robot testbed by: 1) developing, implementing, and evaluating a fuzzy controller for the robotic mechanism, and 2) illustrating that payload variations can have negative effects on the performance of a well designed fuzzy control system. Next, we show how to develop and implement a fuzzy model reference learning controller for the flexible robot and illustrate that it can automatically synthesize a rule-base for a fuzzy controller that will achieve comparable performance to the case where it was manually constructed, and automatically tune the fuzzy controller so that it can adapt to variations in the payload.>

This investigation focuses on the design and implementation of rule-based controllers for end point position control of a two-degree-of-freedom robot with very flexible links. We begin by showing how our intuitive understanding of how to control the robot, that was gained over several years of experience, can be used in the construction of a direct fuzzy controller Next, we investigate the use of a two-level hierarchical rule-based controller with a simple upper-level "expert controller" that captures our knowledge about how to supervise the application of lower-level fuzzy controllers during movements in the robot workspace. Overall, the rule-based supervisory control results have proven to be extremely effective for vibration suppression in the laboratory test bed of this study comparing favorably (in terms of performance, design complexity, and implementation issues) to a variety of conventional techniques attempted to date.>