标题: Contact friction compensation for robots using genetic learning algorithms
作者: Liaw, DC
Huang, JT
電控工程研究所
Institute of Electrical and Control Engineering
关键字: constrained robots;genetic algorithms;learning;friction compensation
公开日期: 1-十月-1998
摘要: In this paper, the issues of contact friction compensation for constrained robots are presented. The proposed design consists of two loops. The inner loop is for the inverse dynamics control which linearizes the system by canceling nonlinear dynamics, while the outer loop is for friction compensation. Although various models of friction have been proposed in many engineering applications, frictional force can be modeled by the Coulomb friction pins the viscous force. Based on such a model, an on-line genetic algorithm is proposed to learn the friction coefficients for friction model. The friction compensation control input is also implemented in terms of the friction coefficients to cancel the effect of unknown friction. By the guidance of the fitness function, the genetic learning algorithm searches for the best-fit value in a way like the natural surviving laws. Simulation results demonstrate that the proposed on-line genetic algorithm can achieve good friction compensation even under the conditions of measurement noise and system uncertainty. Moreover, the proposed control scheme is also found to be feasible for friction compensation of friction model with Stribeck effect and position-dependent friction model.
URI: http://hdl.handle.net/11536/31847
ISSN: 0921-0296
期刊: JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
Volume: 23
Issue: 2-4
起始页: 331
结束页: 349
显示于类别:Articles


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