TY - GEN
T1 - Simulation of fine gain tuning using genetic algorithms for model-based robotic servo controllers
AU - Nagata, Fusaomi
AU - Kuribayashi, Katsutoshi
AU - Kiguchi, Kazuo
AU - Watanabe, Keigo
PY - 2007
Y1 - 2007
N2 - Resolved acceleration control method or computed torque method is used for nonlinear control of industrial manipulators, which is composed of a model base portion and a servo portion. The servo portion is a close loop with respect to the position and velocity. On the other hand, the model base portion has the inertia term, gravity term and centrifugal/Coriolis term, which work for canceling the nonlinearity of manipulator. In order to realize high control stability, the position and velocity gains used in the servo portion should be selected suitably. In this paper, a simple but effective fine tuning method after manual tuning is introduced for the position and velocity feedback gains in the servo portion. At the first step, base values of the gains are roughly selected by a controller designer, e.g., considering the critically damped condition. After that, the base values are finely tuned by genetic algorithms. Genetic algorithms search for the better combination of the position and velocity gains. Simulations are conducted using a dynamic model of PUMA560 manipulator to validate the effectiveness of the proposed method.
AB - Resolved acceleration control method or computed torque method is used for nonlinear control of industrial manipulators, which is composed of a model base portion and a servo portion. The servo portion is a close loop with respect to the position and velocity. On the other hand, the model base portion has the inertia term, gravity term and centrifugal/Coriolis term, which work for canceling the nonlinearity of manipulator. In order to realize high control stability, the position and velocity gains used in the servo portion should be selected suitably. In this paper, a simple but effective fine tuning method after manual tuning is introduced for the position and velocity feedback gains in the servo portion. At the first step, base values of the gains are roughly selected by a controller designer, e.g., considering the critically damped condition. After that, the base values are finely tuned by genetic algorithms. Genetic algorithms search for the better combination of the position and velocity gains. Simulations are conducted using a dynamic model of PUMA560 manipulator to validate the effectiveness of the proposed method.
UR - http://www.scopus.com/inward/record.url?scp=34948819232&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34948819232&partnerID=8YFLogxK
U2 - 10.1109/CIRA.2007.382914
DO - 10.1109/CIRA.2007.382914
M3 - Conference contribution
AN - SCOPUS:34948819232
SN - 1424407907
SN - 9781424407903
T3 - Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007
SP - 196
EP - 201
BT - Proceedings of the 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007
T2 - 2007 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA 2007
Y2 - 20 June 2007 through 23 June 2007
ER -