Neural Network Damping Ratio Model and Admittance Control of Industrial Robot
In the grinding process of industrial robot,the environmental stiffness changes with the unknown environment will have a negative impact to the force control accuracy.In view of the problem of environmental stiffness changes,an adaptive admit-tance control approach based on damping ratio model of neural network is proposed.In the admittance control design,according to the mechanism relationship between force error and system damping,the activation function is designed,and the damping ra-tio model of neural network is constructed.Through this model,the damping ratio can be adjusted online to adapt to the stiffness change of the terminal environment,and the admittance control of force-to-position adaptive conversion is realized.Compared with the conventional admittance control,the simulation results show that the proposed force control scheme has lower force error,faster response speed,and can adapt to the unknown grinding environment with variable stiffness.
Unknown EnvironmentAdmittance ControlNeural Network Damping Ratio ModelAdaptive Cont-rolIndustrial Robot
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