Adaptive Impedance Control in Uncertain Environment for Uncertain Manipulator

Abstract

When the environment information cannot be accurately obtained, large force tracking error and overshoot may exist if traditional constant impedance control is adopted. To solve this problem, a new adaptive impedance controller is designed by combining the compensation and the optimization. In order to track the desired force, the environment location is estimated and the estimation error is compensated. The gradient descent method is used to calculate the impedance variation to reduce the force overshoot, and the Radial Basis Function Neural Network (RBFNN) is introduced to improve the position tracking performance. The effectiveness of the proposed controller is verified by simulations in multiple uncertain environments.