TY - GEN
T1 - Impedance model force control using a neural network-based effective stiffness estimator
AU - Nagata, F.
AU - Mizobuchi, T.
AU - Hase, T.
AU - Haga, Z.
AU - Watanabe, K.
AU - Habib, Maki K.
PY - 2010
Y1 - 2010
N2 - In manufacturing industries of metallic molds, various NC machine tools are used. We have already proposed a desktop NC machine tool with compliance control capability to automatically cope with the finishing process of LED lens molds. The NC machine tool has an ability to control the polishing force acting between an abrasive tool and a workpiece. The force control method is called impedance model force control. The most effective gain is the desired damping of the impedance model. Ideally, the desired damping is calculated from the critical damping condition in consideration of the effective stiffness in force control system. However, there exists a problem that the effective stiffness of the NC machine tool has undesirable nonlinearity. The nonlinearity gives bad influences to the force control stability. In this paper, a fine tuning method of the desired damping is considered by using neural networks. The neural networks acquire the nonlinearity of effective stiffness. The promise is evaluated through an experiment.
AB - In manufacturing industries of metallic molds, various NC machine tools are used. We have already proposed a desktop NC machine tool with compliance control capability to automatically cope with the finishing process of LED lens molds. The NC machine tool has an ability to control the polishing force acting between an abrasive tool and a workpiece. The force control method is called impedance model force control. The most effective gain is the desired damping of the impedance model. Ideally, the desired damping is calculated from the critical damping condition in consideration of the effective stiffness in force control system. However, there exists a problem that the effective stiffness of the NC machine tool has undesirable nonlinearity. The nonlinearity gives bad influences to the force control stability. In this paper, a fine tuning method of the desired damping is considered by using neural networks. The neural networks acquire the nonlinearity of effective stiffness. The promise is evaluated through an experiment.
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M3 - Conference contribution
AN - SCOPUS:84866647300
SN - 9784990288044
T3 - Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10
SP - 697
EP - 700
BT - Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10
T2 - 15th International Symposium on Artificial Life and Robotics, AROB '10
Y2 - 4 February 2010 through 6 February 2010
ER -