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Sensitivity-Aware Model Predictive Control for Robots With Parametric Uncertainty

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Bibliographic Details
Title: Sensitivity-Aware Model Predictive Control for Robots With Parametric Uncertainty
Authors: Tommaso Belvedere, Marco Cognetti, Giuseppe Oriolo, Paolo Robuffo Giordano
Contributors: Marchand, Eric
Source: IEEE Transactions on Robotics. 41:3039-3058
Publisher Information: Institute of Electrical and Electronics Engineers (IEEE), 2025.
Publication Year: 2025
Subject Terms: [SPI.AUTO] Engineering Sciences [physics]/Automatic, model predictive control (MPC), optimization and optimal control, Aerial systems, Aerial Systems: Mechanics and Control, Model Predictive Control, Optimization and Optimal Control, Robust/Adaptive Control of Robotic Systems, mechanics and control, robust/adaptive control of robotic systems
Description: This paper introduces a computationally efficient robust Model Predictive Control (MPC) scheme for controlling nonlinear systems affected by parametric uncertainties in their models. The approach leverages the recent notion of closedloop state sensitivity and the associated ellipsoidal tubes of perturbed trajectories for taking into account online time-varying restrictions on state and input constraints. This makes the MPC controller "aware" of potential additional requirements needed to cope with parametric uncertainty, thus significantly improving the tracking performance and success rates during navigation in constrained environments. One key contribution lies in the introduction of a computationally efficient robust MPC formulation with a comparable computational complexity to a standard MPC (i.e., an MPC not explicitly dealing with parametric uncertainty). An extensive simulation campaign is presented to demonstrate the effectiveness of the proposed approach in handling parametric uncertainties and enhancing task performance, safety, and overall robustness. Furthermore, we also provide an experimental validation that shows the feasibility of the approach in real-world conditions and corroborates the statistical findings of the simulation campaign. The versatility and efficiency of the proposed method make it therefore a valuable tool for real-time control of robots subject to non-negligible uncertainty in their models.
Document Type: Article
File Description: application/pdf
ISSN: 1941-0468
1552-3098
DOI: 10.1109/tro.2025.3554415
Access URL: https://hdl.handle.net/11573/1738723
https://doi.org/10.1109/TRO.2025.3554415
Rights: IEEE Copyright
CC BY
Accession Number: edsair.doi.dedup.....f9d8f8d3adb9e0e231d2443fb6b1340b
Database: OpenAIRE
Description
Abstract:This paper introduces a computationally efficient robust Model Predictive Control (MPC) scheme for controlling nonlinear systems affected by parametric uncertainties in their models. The approach leverages the recent notion of closedloop state sensitivity and the associated ellipsoidal tubes of perturbed trajectories for taking into account online time-varying restrictions on state and input constraints. This makes the MPC controller "aware" of potential additional requirements needed to cope with parametric uncertainty, thus significantly improving the tracking performance and success rates during navigation in constrained environments. One key contribution lies in the introduction of a computationally efficient robust MPC formulation with a comparable computational complexity to a standard MPC (i.e., an MPC not explicitly dealing with parametric uncertainty). An extensive simulation campaign is presented to demonstrate the effectiveness of the proposed approach in handling parametric uncertainties and enhancing task performance, safety, and overall robustness. Furthermore, we also provide an experimental validation that shows the feasibility of the approach in real-world conditions and corroborates the statistical findings of the simulation campaign. The versatility and efficiency of the proposed method make it therefore a valuable tool for real-time control of robots subject to non-negligible uncertainty in their models.
ISSN:19410468
15523098
DOI:10.1109/tro.2025.3554415