Publication

Using Neuroevolution for Optimal Impedance Control

José de Gea Fernández, Frank Kirchner

In: 13th IEEE International Conference on Emerging Technologies and Factory Automation. IEEE International Conference on Emerging Technologies and Factory Automation (ETFA-2008) September 15-18 Hamburg Germany Pages 1063-1066 IEEE 2008.

Abstract

This paper describes the use of evolutionary algorithms to find an optimal solution for the parameters of an impedance controller represented as an artificial neural network (ANN). An impedance controller with force tracking capabilities has been evolved using evolutionary strategies which control the forces between a robotic manipulator and the environment. Simulation results show the controller’s performance using a model of a two-link robot arm and a Hunt-Crossley non-linear model of the environment.

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz