Name
Abstract | ||
|---|---|---|
Off-line robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is calculated with torque and position sampled data while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. This method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. A new method called DIDIM (Direct and Inverse Dynamic Identification Models) has been proposed and validated on a 2 degree-of freedom robot [1]. DIDIM method requires only the joint force/torque measurement. It is based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference trajectory for both the actual and the simulated robot. The optimal parameters minimize the 2-norm of the error between the actual force/torque and the simulated force/torque. A validation experiment on a 6 dof Staubli TX40 robot shows that DIDIM method is very efficient on industrial robots. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICRA.2011.5980425 | Robotics and Automation |
Keywords | Field | DocType |
acceleration control,band-pass filters,closed loop systems,force measurement,industrial robots,least squares approximations,mobile robots,parameter estimation,position control,position measurement,robot dynamics,torque measurement,velocity control,DIDIM method,Staubli TX40 robot,acceleration estimation,bandpass filtering,closed-loop simulation,freedom robot,industrial robot,joint force measurement,joint position data,joint torque measurement,joint velocity estimation,linear least-squares technique,off-line robot dynamic identification method,parameter estimation,position measurement,position sampled data,simulated robot,torque sampled data,trajectory tracking | Torque,Control theory,Simulation,Control engineering,System dynamics,Acceleration,Sampling (statistics),Engineering,Estimation theory,Robot,Trajectory,Mobile robot | Conference |
Volume | Issue | ISSN |
2011 | 1 | 1050-4729 |
ISBN | Citations | PageRank |
978-1-61284-386-5 | 5 | 0.55 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maxime Gautier | 1 | 477 | 76.28 |
| P. O. Vandanjon | 2 | 26 | 3.23 |
| Alexandre Janot | 3 | 86 | 12.37 |