Neural and Neurofuzzy FELA Adaptive Robot Control Using Feedforward and Counterpropagation Networks

Tzafestas, SG; Rigatos, GG

dc.contributor.author	Tzafestas, SG	en
dc.contributor.author	Rigatos, GG	en
dc.date.accessioned	2014-03-01T01:13:55Z
dc.date.available	2014-03-01T01:13:55Z
dc.date.issued	1998	en
dc.identifier.issn	0921-0296	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/12790
dc.subject	Adaptive robot control	en
dc.subject	Counterpropagation network	en
dc.subject	Feedforward neural network	en
dc.subject	Fuzzy control	en
dc.subject	Neural control	en
dc.subject	Neurofuzzy control	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Robotics	en
dc.subject.other	Adaptive control systems	en
dc.subject.other	Algorithms	en
dc.subject.other	Backpropagation	en
dc.subject.other	Computational complexity	en
dc.subject.other	Feedforward neural networks	en
dc.subject.other	Fuzzy control	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Problem solving	en
dc.subject.other	Robot learning	en
dc.subject.other	Torque control	en
dc.subject.other	Counter propagation network-based fuzzy controllers (CPN-FC)	en
dc.subject.other	Manipulators	en
dc.title	Neural and Neurofuzzy FELA Adaptive Robot Control Using Feedforward and Counterpropagation Networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1023/A:1008077807191	en
heal.identifier.secondary	http://dx.doi.org/10.1023/A:1008077807191	en
heal.language	English	en
heal.publicationDate	1998	en
heal.abstract	In this paper, the application of neural networks and neurofuzzy systems to the control of robotic manipulators is examined. Two main control structures are presented in a comparative manner. The first is a Counter Propagation Network-based Fuzzy Controller (CPN-FC) which is able to self-organize and correct on-line its rule base. The self-tuning capability of the fuzzy logic controller is attained by taking advantage of the structural equivalence between the fuzzy logic controller and a counterpropagation network. The second control structure is a more familiar neural adaptive controller based on a feedforward (MLP) network. The neural controller learns the inverse dynamics of the robot joints, and gradually eliminates the model uncertainties and disturbances. Both schemes cooperate with the computed torque control algorithm, and in that way the reduction of their complexity is achieved. The ability of adaptive fuzzy systems to compete with neural networks in difficult control problems is demonstrated. A sufficient set of numerical results is included.	en
heal.publisher	KLUWER ACADEMIC PUBL	en
heal.journalName	Journal of Intelligent and Robotic Systems: Theory and Applications	en
dc.identifier.doi	10.1023/A:1008077807191	en
dc.identifier.isi	ISI:000077318300011	en
dc.identifier.volume	23	en
dc.identifier.issue	2-4	en
dc.identifier.spage	291	en
dc.identifier.epage	330	en