In this article, we present an intrinsically stable Model Predictive Control (IS-MPC) framework for humanoid gait generation that incorporates a stability constraint in the formulation. The method uses as prediction model a dynamically extended Linear Inverted Pendulum with Zero Moment Point (ZMP) velocities as control inputs, producing in real time a gait (including footsteps with timing) that realizes omnidirectional motion commands coming from an external source. The stability constraint links future ZMP velocities to the current state so as to guarantee that the generated Center of Mass (CoM) trajectory is bounded with respect to the ZMP trajectory. Being the MPC control horizon finite, only part of the future ZMP velocities are decision variables; the remaining part, called tail, must be either conjectured or anticipated using preview information on the reference motion. Several options for the tail are discussed, each corresponding to a specific terminal constraint. A feasibility analysis of the generic MPC iteration is developed and used to obtain sufficient conditions for recursive feasibility. Finally, we prove that recursive feasibility guarantees stability of the CoM/ZMP dynamics. Simulation and experimental results on NAO and HRP-4 are presented to highlight the performance of IS-MPC.

MPC for Humanoid Gait Generation: Stability and Feasibility / Scianca, Nicola; DE SIMONE, Daniele; Lanari, Leonardo; Oriolo, Giuseppe. - In: IEEE TRANSACTIONS ON ROBOTICS. - ISSN 1552-3098. - 5:4(2020), pp. 1171-1188. [10.1109/TRO.2019.2958483]

MPC for Humanoid Gait Generation: Stability and Feasibility

Nicola Scianca;Daniele De Simone;Leonardo Lanari;Giuseppe Oriolo
2020

Abstract

In this article, we present an intrinsically stable Model Predictive Control (IS-MPC) framework for humanoid gait generation that incorporates a stability constraint in the formulation. The method uses as prediction model a dynamically extended Linear Inverted Pendulum with Zero Moment Point (ZMP) velocities as control inputs, producing in real time a gait (including footsteps with timing) that realizes omnidirectional motion commands coming from an external source. The stability constraint links future ZMP velocities to the current state so as to guarantee that the generated Center of Mass (CoM) trajectory is bounded with respect to the ZMP trajectory. Being the MPC control horizon finite, only part of the future ZMP velocities are decision variables; the remaining part, called tail, must be either conjectured or anticipated using preview information on the reference motion. Several options for the tail are discussed, each corresponding to a specific terminal constraint. A feasibility analysis of the generic MPC iteration is developed and used to obtain sufficient conditions for recursive feasibility. Finally, we prove that recursive feasibility guarantees stability of the CoM/ZMP dynamics. Simulation and experimental results on NAO and HRP-4 are presented to highlight the performance of IS-MPC.
2020
Gait generation; humanoid robots; internal stability; legged locomotion; predictive control; recursive feasibility
01 Pubblicazione su rivista::01a Articolo in rivista
MPC for Humanoid Gait Generation: Stability and Feasibility / Scianca, Nicola; DE SIMONE, Daniele; Lanari, Leonardo; Oriolo, Giuseppe. - In: IEEE TRANSACTIONS ON ROBOTICS. - ISSN 1552-3098. - 5:4(2020), pp. 1171-1188. [10.1109/TRO.2019.2958483]
File allegati a questo prodotto
File Dimensione Formato  
Scianca_MPC_2020.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 3.55 MB
Formato Adobe PDF
3.55 MB Adobe PDF   Contatta l'autore
Scianca_Postprint_MPC_2019.pdf

accesso aperto

Note: https://ieeexplore.ieee.org/document/8955951
Tipologia: Documento in Post-print (versione successiva alla peer review e accettata per la pubblicazione)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 4.36 MB
Formato Adobe PDF
4.36 MB Adobe PDF

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/11573/1396043
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 50
  • ???jsp.display-item.citation.isi??? 38
social impact