Neural network adaptive impedance control based on stiffness damping characteristics
A neural network adaptive impedance control method based on stiffness damping characteristics was proposed to address the problem that the force tracking performance of impedance control for sanding robots was affected by the unknown en-vironmental stiffness and the change of environmental position.Since the reference trajectory is not easy to be determined due to the unknown environmental parameters,an adaptive PI control law was constructed to compensate the reference trajectory and re-duce the steady-state error of force tracking;in order to improve the dynamic performance of the force tracking control,according to the uniform regulation law of the force error on the stiffness coefficient and damping coefficient—stiffness damping characteris-tics,and combined with that the force error has the characteristics of time-varying and non-linear,an activation function describing the relationship between force error and stiffness damping characteristics was designed,and an adaptive impedance parameter neu-ral network model was constructed,whose outputs were stiffness coefficient and damping coefficient,to ensure the suppleness of force tracking control through the impedance control based on the fusion of the reference trajectory compensation and an adaptive impedance parameter neural network model.The simulation results show that the proposed adaptive impedance control method has better force tracking effect than the traditional impedance control and the impedance control with reference trajectory PI compen-sation.
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