ABSTRACT
Dept, of Electrical Engineering, The Ohio State University, 2015 Neil Avenue, Columbus OH 43210-1272
A design and implementation case-study that focuses on endpoint position control of a two-degree-of-freedom robot with very flexible links is presented. Linear and nonlinear conventional control techniques have been shown to be somewhat successful in achieving various control objectives for the laboratory test beds of this and many other studies; however their reliance on an accurate mathematical model of the process often limits their chances of achieving very good endpoint position control. Here, we investigate an alternative to conventional approaches where we employ rule-based controllers to represent and implement two general forms of knowledge that we have about how to best control the mechanism: (i) experience gained from the use of a mathematical model and conventional control; and (ii) an intuitive understanding of the dynamics of the two-link flexible robot. We begin the case study by assuming that the controls for the two links of the robotic mechanism can be designed and implemented independently and investigate the performance of rule-based fuzzy controllers which only use simple intuitive knowledge about how to control two independent links. Next we show that if the rule-base is augmented with knowledge about the coupling effects between the two links, the controller can achieve improved
performance over the uncoupled case. We show, however, 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” (FMRLC) [1, 2, 3, 4] for the flexible robot and illustrate that it can: (i) automatically synthesize a rule-base for a fuzzy controller that will achieve comparable performance to the case where it was manually constructed, and (ii) automatically tune the fuzzy controller so that it can adapt to variations in the payload so that it can perform better than the manually constructed fuzzy controller. The final portion of our case study investigates 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 low-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 (including linear robust designs, feedback linearization, input command shaping, and adaptive approaches).
