This article proposes a new framework for the computational design of robots that are robust to disturbances. The framework combines trajectory optimization (TO) and feedback control design to produce robots with improved performance under perturbations by co-optimizing a nominal trajectory alongside a feedback policy and the system morphology. Stochastic programming (SP) methods are used to address these perturbations via uncertainty models in the problem specification, resulting in motions that are easier to stabilize via feedback. Two robotic systems serve to demonstrate the potential of the method: a planar manipulator and a jumping monopod robot. The co-optimized robots achieve higher performance compared to state-of-the-art solutions where the feedback controller is designed separately from the physical system. Specifically, the co-designed controllers show higher tracking accuracy and improved energy efficiency (e.g., 91% decrease in tracking error and approximate to 5% decrease in energy consumption for a manipulator) compared to linear quadratic regulator applied to a design optimized for nominal conditions.

Robust Co-Design: Coupling Morphology and Feedback Design Through Stochastic Programming / Bravo-Palacios, Gabriel; Grandesso, Gianluigi; Del Prete, Andrea; Wensing, Patrick M.. - In: JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT AND CONTROL. - ISSN 0022-0434. - 144:2(2022), pp. 021007.1-021007.12. [10.1115/1.4052463]

Robust Co-Design: Coupling Morphology and Feedback Design Through Stochastic Programming

Grandesso, Gianluigi;Del Prete, Andrea;
2022-01-01

Abstract

This article proposes a new framework for the computational design of robots that are robust to disturbances. The framework combines trajectory optimization (TO) and feedback control design to produce robots with improved performance under perturbations by co-optimizing a nominal trajectory alongside a feedback policy and the system morphology. Stochastic programming (SP) methods are used to address these perturbations via uncertainty models in the problem specification, resulting in motions that are easier to stabilize via feedback. Two robotic systems serve to demonstrate the potential of the method: a planar manipulator and a jumping monopod robot. The co-optimized robots achieve higher performance compared to state-of-the-art solutions where the feedback controller is designed separately from the physical system. Specifically, the co-designed controllers show higher tracking accuracy and improved energy efficiency (e.g., 91% decrease in tracking error and approximate to 5% decrease in energy consumption for a manipulator) compared to linear quadratic regulator applied to a design optimized for nominal conditions.
2022
2
Bravo-Palacios, Gabriel; Grandesso, Gianluigi; Del Prete, Andrea; Wensing, Patrick M.
Robust Co-Design: Coupling Morphology and Feedback Design Through Stochastic Programming / Bravo-Palacios, Gabriel; Grandesso, Gianluigi; Del Prete, Andrea; Wensing, Patrick M.. - In: JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT AND CONTROL. - ISSN 0022-0434. - 144:2(2022), pp. 021007.1-021007.12. [10.1115/1.4052463]
File in questo prodotto:
File Dimensione Formato  
Robust_CoDesign.pdf

Solo gestori archivio

Tipologia: Post-print referato (Refereed author’s manuscript)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.28 MB
Formato Adobe PDF
2.28 MB Adobe PDF   Visualizza/Apri
ds_144_02_021007.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.79 MB
Formato Adobe PDF
2.79 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/336369
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 7
social impact