Using Neuroevolution for Optimal Impedance Control
In 13th IEEE International Conference on Emerging Technologies and Factory Automation, (ETFA-2008), 15.9.-18.9.2008, Hamburg, IEEE, pages 1063-1066, 2008.
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.