Constrained dynamic parameter estimation using the Extended Kalman Filter

In this paper we present a real-time method for identification of the dynamic parameters of a manipulator and its load using kinematic measurements and either joint torques or force and moment at the base. The parameters are estimated using the Extended Kalman Filter and constraints are imposed using Sigmoid functions to ensure the parameters remain within their physically feasible ranges, such as links having positive masses and moments of inertia. Identified parameters can be used in model based controllers. The presented approach is validated through simulation and on data collected with the Barret WAM manipulator. Using the estimated parameters instead of ones provided by the manufacturer greatly improves joint torque prediction.

Authors

• Vladimir Joukov
• Vincent Bonnet
• Gentiane Venture
• Dana Kulic

Keywords

• Mathematical model
• Manipulator dynamics
• Torque
• Noise measurement
• Kalman filters
• Parameter Estimates
• Kinetic Parameters
• Sigmoid Function
• Moment Of Inertia
• Joint Torque
• Kinematic Measures
• Center Of Mass
• Equations Of Motion
• Measurement Noise
• Friction Coefficient
• Kalman Filter
• Joint Position
• Process Noise
• End-effector
• Measurement Vector
• Extended Kalman Filter
• Noisy Measurements
• Mass Estimates
• Weighted Least Squares
• Torque Measurements
• Inertial Parameters
• First-order Moment
• Inertia Tensor
• Link Parameters
• Joint Trajectories
• Robot Structure
• Real Robot
• CAD Model
• Off-diagonal
• Finite Series