Modified transpose Jacobian control of robotic systems

Moosavian, SAA; Papadopoulos, E

dc.contributor.author	Moosavian, SAA	en
dc.contributor.author	Papadopoulos, E	en
dc.date.accessioned	2014-03-01T01:26:42Z
dc.date.available	2014-03-01T01:26:42Z
dc.date.issued	2007	en
dc.identifier.issn	0005-1098	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18177
dc.subject	Control	en
dc.subject	Path tracking	en
dc.subject	Robotics	en
dc.subject	Stability	en
dc.subject	Transpose Jacobian algorithm	en
dc.subject.classification	Automation & Control Systems	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Algorithms	en
dc.subject.other	Computational methods	en
dc.subject.other	Jacobian matrices	en
dc.subject.other	Manipulators	en
dc.subject.other	Motion planning	en
dc.subject.other	Path tracking	en
dc.subject.other	Transpose Jacobian algorithm	en
dc.subject.other	Robotics	en
dc.title	Modified transpose Jacobian control of robotic systems	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.automatica.2006.12.029	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.automatica.2006.12.029	en
heal.language	English	en
heal.publicationDate	2007	en
heal.abstract	The simplicity of Transpose Jacobian (TJ) control is a significant characteristic of this algorithm for controlling robotic manipulators. Nevertheless, a poor performance may result in tracking of fast trajectories, since it is not dynamics-based. Use of high gains can deteriorate performance seriously in the presence of feedback measurement noise. Another drawback is that there is no prescribed method of selecting its control gains. In this paper, based on feedback linearization approach a Modified TJ (MTJ) algorithm is presented which employs stored data of the control command in the previous time step, as a learning tool to yield improved performance. The gains of this new algorithm can be selected systematically, and do not need to be large, hence the noise rejection characteristics of the algorithm are improved. Based on Lyapunov's theorems, it is shown that both the standard and the MTJ algorithms are asymptotically stable. Analysis of the required computational effort reveals the efficiency of the proposed MTJ law compared to the Model-based algorithms. Simulation results are presented which compare tracking performance of the MTJ algorithm to that of the TJ and Model-Based algorithms in various tasks. Results of these simulations show that performance of the new MTJ algorithm is comparable to that of Computed Torque algorithms, without requiring a priori knowledge of plant dynamics, and with reduced computational burden. Therefore, the proposed algorithm is well suited to most industrial applications where simple efficient algorithms are more appropriate than complicated theoretical ones with massive computational burden. (c) 2007 Elsevier Ltd. All rights reserved.	en
heal.publisher	PERGAMON-ELSEVIER SCIENCE LTD	en
heal.journalName	Automatica	en
dc.identifier.doi	10.1016/j.automatica.2006.12.029	en
dc.identifier.isi	ISI:000247764400011	en
dc.identifier.volume	43	en
dc.identifier.issue	7	en
dc.identifier.spage	1226	en
dc.identifier.epage	1233	en