Fuzzy learning control for a flexible-link robot

Fuzzy control has emerged as a practical alternative to several conventional control schemes since it has shown success in some application areas. However, several important issues remain, including: (i) 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 (ii) the fuzzy controller constructed for the nominal plant may later perform inadequately if significant and unpredictable plant parameter variations occur. In this paper we show how to develop and implement a "fuzzy model reference learning controller" (FMRLC) [1-4] for a robot with very flexible links. Towards addressing the issues mentioned above, we show that the FMRLC approach 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.