Adaptive stiffness estimation for compliant robotic manipulation using stochastic disturbance models

Abstract

To achieve haptic telepresence and proper contact behaviour, the control action of a robotic manipulator must be designed with respect to contact parameters. Unfortunately, it is hard to know these parameters exactly in unknown or partly known environments. In this case, contact instability and poor dynamic accuracy can arise due to the presence of modelling errors in the control design. To overcome these problems, online estimation of the relevant contact parameters can be performed, with corresponding adaptation of control laws. This article presents an algorithm for online stiffness estimation for compliant robotic manipulation based on the extended state-space representation of the system and force signals. No position or velocity measurements are required. The algorithm, supported by theoretical analysis, uses offline data concerning several stiffness mismatch scenarios and, through a least square error analysis, computes an estimate of the stiffness value. Simulation results are presented, with fast and accurate estimation even in the presence of noise, highlighting the merits of the method.