Intelligent Impedance Control using Wavelet Neural Network for dynamic contact force tracking in unknown varying environments

In this paper, the Intelligent Impedance Control based Wavelet Neural Network (IIC-WNN) is introduced as a noble adaptive variable impedance approach to enhance the efficiency of tracking the desired force and interaction with varying unknown (in terms of unknown stiffness and unknown geometric) environment. In the proposed method, a systematic online adaptation mechanism using the wavelet neural network is presented to adapt the impedance parameter according to a variable environment. Using the introduced adaptive law, the robot would be able to track the desired force on the moving environment with the unknown stiffness. Unlike the general impedance control which the position and stiffness of the environment need to be available and known for choosing the impedance parameters, the proposed structure for the impedance equation leads to adapt the impedance parameters according to the interaction environment. In addition, the stability conditions and adaptation laws using Lyapunov's method for the variable impedance are given to guarantee the force tracking and stability of the closed-loop system. Finally, various numerical and experimental results verify the performance of the proposed adaptive approach. The experimental results strongly prove that the presented method has a better force tracking performance than the general impedance with constant parameters.