A Novel Formulation for Adaptive Computed Torque Control Enabling Low Feedback Gains in Highly Dynamical Tasks
Computed Torque Control is a widely used control strategy for ensuring precise trajectory tracking and impedance behavior in robotic manipulators. However, because it relies on feedback linearization using the robot’s dynamic model, any inaccuracies in the model can adversely affect track...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10967496/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850155057189748736 |
|---|---|
| author | Giorgio Simonini Marco Baracca Tommaso V. Cavaliere Antonio Bicchi Paolo Salaris |
| author_facet | Giorgio Simonini Marco Baracca Tommaso V. Cavaliere Antonio Bicchi Paolo Salaris |
| author_sort | Giorgio Simonini |
| collection | DOAJ |
| description | Computed Torque Control is a widely used control strategy for ensuring precise trajectory tracking and impedance behavior in robotic manipulators. However, because it relies on feedback linearization using the robot’s dynamic model, any inaccuracies in the model can adversely affect tracking performance. This effect is even more visible when low feedback gains are used to impose compliance in the robot’s behavior. Adaptive Computed Torque Control addresses this issue by updating the dynamic model parameters to achieve asymptotic stability. Nevertheless, the classical update law requires the inversion of the estimated mass matrix, potentially leading to numerical stability problems. Several approaches, such as Adaptive Inertia-Related Control, were proposed to overcome this problem. However, they have problems in guaranteeing the desired impedance behaviour. In this work, we present a novel adaptive computed torque control law formulated both in joint space and Cartesian space, and we provide theoretical proofs of its asymptotic stability. We validate the proposed controller through simulations and real-robot experiments involving various dynamic motions. Finally, we demonstrate its effectiveness in a real dynamic task: throwing an unknown object. |
| format | Article |
| id | doaj-art-1ee240a1b6964d5f9c8c0b4927e35ad7 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1ee240a1b6964d5f9c8c0b4927e35ad72025-08-20T02:25:03ZengIEEEIEEE Access2169-35362025-01-0113698986990910.1109/ACCESS.2025.356163510967496A Novel Formulation for Adaptive Computed Torque Control Enabling Low Feedback Gains in Highly Dynamical TasksGiorgio Simonini0https://orcid.org/0009-0006-4696-5889Marco Baracca1https://orcid.org/0000-0002-5724-4159Tommaso V. Cavaliere2Antonio Bicchi3https://orcid.org/0000-0001-8635-5571Paolo Salaris4https://orcid.org/0000-0003-1313-5898Dipartimento di Ingegneria dell’Informazione e Centro di Ricerca “Enrico Piaggio”, Università di Pisa, Pisa, ItalyDipartimento di Ingegneria dell’Informazione e Centro di Ricerca “Enrico Piaggio”, Università di Pisa, Pisa, ItalyDipartimento di Ingegneria dell’Informazione e Centro di Ricerca “Enrico Piaggio”, Università di Pisa, Pisa, ItalyDipartimento di Ingegneria dell’Informazione e Centro di Ricerca “Enrico Piaggio”, Università di Pisa, Pisa, ItalyDipartimento di Ingegneria dell’Informazione e Centro di Ricerca “Enrico Piaggio”, Università di Pisa, Pisa, ItalyComputed Torque Control is a widely used control strategy for ensuring precise trajectory tracking and impedance behavior in robotic manipulators. However, because it relies on feedback linearization using the robot’s dynamic model, any inaccuracies in the model can adversely affect tracking performance. This effect is even more visible when low feedback gains are used to impose compliance in the robot’s behavior. Adaptive Computed Torque Control addresses this issue by updating the dynamic model parameters to achieve asymptotic stability. Nevertheless, the classical update law requires the inversion of the estimated mass matrix, potentially leading to numerical stability problems. Several approaches, such as Adaptive Inertia-Related Control, were proposed to overcome this problem. However, they have problems in guaranteeing the desired impedance behaviour. In this work, we present a novel adaptive computed torque control law formulated both in joint space and Cartesian space, and we provide theoretical proofs of its asymptotic stability. We validate the proposed controller through simulations and real-robot experiments involving various dynamic motions. Finally, we demonstrate its effectiveness in a real dynamic task: throwing an unknown object.https://ieeexplore.ieee.org/document/10967496/Adaptive controlparameter estimationcollaborative robotsthrowing of objects |
| spellingShingle | Giorgio Simonini Marco Baracca Tommaso V. Cavaliere Antonio Bicchi Paolo Salaris A Novel Formulation for Adaptive Computed Torque Control Enabling Low Feedback Gains in Highly Dynamical Tasks IEEE Access Adaptive control parameter estimation collaborative robots throwing of objects |
| title | A Novel Formulation for Adaptive Computed Torque Control Enabling Low Feedback Gains in Highly Dynamical Tasks |
| title_full | A Novel Formulation for Adaptive Computed Torque Control Enabling Low Feedback Gains in Highly Dynamical Tasks |
| title_fullStr | A Novel Formulation for Adaptive Computed Torque Control Enabling Low Feedback Gains in Highly Dynamical Tasks |
| title_full_unstemmed | A Novel Formulation for Adaptive Computed Torque Control Enabling Low Feedback Gains in Highly Dynamical Tasks |
| title_short | A Novel Formulation for Adaptive Computed Torque Control Enabling Low Feedback Gains in Highly Dynamical Tasks |
| title_sort | novel formulation for adaptive computed torque control enabling low feedback gains in highly dynamical tasks |
| topic | Adaptive control parameter estimation collaborative robots throwing of objects |
| url | https://ieeexplore.ieee.org/document/10967496/ |
| work_keys_str_mv | AT giorgiosimonini anovelformulationforadaptivecomputedtorquecontrolenablinglowfeedbackgainsinhighlydynamicaltasks AT marcobaracca anovelformulationforadaptivecomputedtorquecontrolenablinglowfeedbackgainsinhighlydynamicaltasks AT tommasovcavaliere anovelformulationforadaptivecomputedtorquecontrolenablinglowfeedbackgainsinhighlydynamicaltasks AT antoniobicchi anovelformulationforadaptivecomputedtorquecontrolenablinglowfeedbackgainsinhighlydynamicaltasks AT paolosalaris anovelformulationforadaptivecomputedtorquecontrolenablinglowfeedbackgainsinhighlydynamicaltasks AT giorgiosimonini novelformulationforadaptivecomputedtorquecontrolenablinglowfeedbackgainsinhighlydynamicaltasks AT marcobaracca novelformulationforadaptivecomputedtorquecontrolenablinglowfeedbackgainsinhighlydynamicaltasks AT tommasovcavaliere novelformulationforadaptivecomputedtorquecontrolenablinglowfeedbackgainsinhighlydynamicaltasks AT antoniobicchi novelformulationforadaptivecomputedtorquecontrolenablinglowfeedbackgainsinhighlydynamicaltasks AT paolosalaris novelformulationforadaptivecomputedtorquecontrolenablinglowfeedbackgainsinhighlydynamicaltasks |