Name
Abstract | ||
|---|---|---|
Computed torque control allows the design of considerably more precise, energy-efficient and compliant controls for robots. However, the major obstacle is the requirement of an accurate model for torque generation, which cannot be obtained in some cases using rigid-body formulations due to unmodeled nonlinearities, such as complex friction or actuator dynamics. In such cases, models approximated from robot data present an appealing alternative. In this paper, we compare two non- parametric regression methods for model approximation, i.e., locally weighted projection regression (LWPR) and Gaussian process regression (GPR). While locally weighted regression was employed for real-time model estimation in learning adaptive control, Gaussian process regression has not been used in control to-date due to high computational requirements. The comparison includes the assessment of model approximation for both regression methods using data originated from SARCOS robot arm, as well as an evaluation of the robot tracking performance in computed torque control employing the approximated models. Our results show that GPR can be applied for realtime control achieving higher accuracy. However, for the online learning LWPR is superior by reason of lower computational requirements. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ACC.2008.4586493 | Seattle, WA |
Keywords | DocType | ISSN |
computed torque control,learning adaptive control,complex friction,manipulator dynamics,lwpr,unmodeled nonlinearities,nonparametric regression models,regression analysis,torque control,gaussian process regression,adaptive control,locally weighted projection regression,gaussian processes,gpr,sarcos robot arm,actuator dynamics,real time,rigid body,nonparametric regression,energy efficient,approximation error,robot arm,predictive models,kernel,ground penetrating radar,vectors,non parametric regression,covariance matrix | Conference | 0743-1619 E-ISBN : 978-1-4244-2079-7 |
ISBN | Citations | PageRank |
978-1-4244-2079-7 | 27 | 2.08 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| duy nguyentuong | 1 | 438 | 26.22 |
| Matthias Seeger | 2 | 882 | 76.54 |
| Jan Peters | 3 | 3553 | 264.28 |