Guillaume Mercère

Associate Professor

University of Poitiers

France

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About

Guillaume Mercère is an Associate Professor at Université de Poitiers, France. His research focuses on model identification and performance in robotics, vehicle mass estimation, and control methodologies for dynamic systems. He has contributed significantly to the field through recent articles addressing topics such as data-enabled predictive control and robust estimation techniques.

Articles (18)

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2025

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2025

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2024

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2022

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2021

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2020

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2020

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2020

Collaborators (3)

Alexander Medvedev

Uppsala University

SWEDEN

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Patrick Coirault

Full Professor

University of Poitiers

FRANCE

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Edouard Laroche

University of Strasbourg

FRANCE

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Guillaume Mercère

Research Interests

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Articles (18)

Model identification of a cable-based mechanical transmission for robotics using a Best Linear Approximation approach

Recursive total least squares with improved parameter tracking: Application to model-based vehicle mass estimation

Verifying output amplitude constraints in a robust manner for an identification experiment with a multisine excitation

On the Equivalence Between Functionally Affine LPV State-Space Representations and LFT Models

Active control of road vehicle’s drag for varying upstream flow conditions using a recursive subspace based predictive control methodology

Data enabled Predictive Control of LPV systems

A Robust and Regularized Algorithm for Recursive Total Least Squares Estimation

Enhanced Recursive Total Least Squares Method With Subspace Tracking and Noise Covariance Estimation

Tracking Distributed Parameters System Dynamics with Recursive Dynamic Mode Decomposition with Control

Tracking Distributed Parameters System Dynamics with Recursive Dynamic Mode Decomposition with Control

LPV-ARX representations of LPV state-space models with affine dependence

Minimal Realizations of Input–Output Behaviors by LPV State-Space Representations With Affine Dependence

Model learning of the tire–road friction slip dependency under standard driving conditions

MIMO system identification using common denominator and numerators with known degrees

Noise covariance matrix estimation with subspace model identification for Kalman filtering

Combining linear algebra and numerical optimization for gray-box affine state-space model identification

Combining Linear Algebra and Numerical Optimization for Gray-Box Affine State-Space Model Identification

Foreword Identification and Control in Biomedical Applications

Collaborators (3)

Alexander Medvedev

Patrick Coirault

Edouard Laroche

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