Title :
Using neuroevolution for optimal impedance control
Author :
De Gea, Jose ; Kirchner, Frank
Author_Institution :
Robot. Group, Univ. of Bremen, Bremen
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 controllerpsilas performance using a model of a two-link robot arm and a Hunt-Crossley non-linear model of the environment.
Keywords :
electric impedance; electric variables control; evolutionary computation; force control; manipulators; neurocontrollers; nonlinear control systems; optimal control; Hunt-Crossley nonlinear model; artificial neural network; evolutionary algorithms; force control; force tracking capabilities; neuroevolution; optimal impedance control; robotic manipulator; two-link robot arm; Artificial neural networks; Control systems; Force control; Force measurement; Genetic mutations; Impedance; Intelligent robots; Manipulator dynamics; Neural networks; Optimal control;
Conference_Titel :
Emerging Technologies and Factory Automation, 2008. ETFA 2008. IEEE International Conference on
Conference_Location :
Hamburg
Print_ISBN :
978-1-4244-1505-2
Electronic_ISBN :
978-1-4244-1506-9
DOI :
10.1109/ETFA.2008.4638525
