Parameter estimation process for the dynamic model of robotic manipulators

Abstract

This paper presents a method of estimating the dynamic parameters of robotic manipulators.The benefits of using the method called the modified Newton-Euler formula to determine the dynamic model of robotic arms are explained. It is shown, that the nonlinear model can be transformed into a linear-in-parameters model and the determination of the independently identifiable variables is explained.The differences between the applicability and efficiency of three estimators are presented, namely between the least-squares, weighted least-squares, and the maximum likelihood estimators. Several criteria are introduced to optimize the excitation trajectories. In a deterministic framework, the Frobenius norm as the condition number of the regression matrix can be used as the cost function to be minimized with a conditional nonlinear optimization algorithm, taken into account the limited joint angles, velocities, and accelerations.If the noises of the joint angles are not negligible, then the maximum likelihood estimator can be used in a stochastic framework and an iterative optimization can be applied to minimize the Cramér-Rao lower bound of the estimation.