Automatica (Journal of IFAC)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Robot Dynamics and Control
Predictive reference shaping for constrained robotic systems using evolutionary algorithms
Applied Soft Computing
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
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In this paper, the issues of contact friction compensation forconstrained robots are presented. The proposed design consists of two loops.The inner loop is for the inverse dynamics control which linearizes thesystem by canceling nonlinear dynamics, while the outer loop is for frictioncompensation. Although various models of friction have been proposed in manyengineering applications, frictional force can be modeled by the Coulombfriction plus the viscous force. Based on such a model, an on-line geneticalgorithm is proposed to learn the friction coefficients for friction model.The friction compensation control input is also implemented in terms of thefriction coefficients to cancel the effect of unknown friction. By theguidance of the fitness function, the genetic learning algorithm searchesfor the best-fit value in a way like the natural surviving laws. Simulationresults demonstrate that the proposed on-line genetic algorithm can achievegood friction compensation even under the conditions of measurement noiseand system uncertainty. Moreover, the proposed control scheme is also foundto be feasible for friction compensation of friction model with Stribeckeffect and position-dependent friction model.