Dynamic control of a robot arm using CMAC neural networks

Abstract

Neural identification and control techniques are well-suited to the problem of controlling robot dynamics. This paper describes the use of CMAC networks for the adaptive dynamic control of an orange-harvesting robot. Among the various neural-network paradigms available, the CMAC model was chosen in this case because of its fast convergence and on-line adaptation capability. The solution of this dynamic control problem with CMAC is an encouraging demonstration of “experience-based”, as opposed to model-based, control techniques and is a good example of the use of on-line learning in adaptive neural control.