In modern manufacturing, industrial robots are essential components that allow saving cost, increase quality and productivity for instance. To achieve such goals, high accuracy and speed are simultaneously required. The design of control laws compliant with such requirements demands high-fidelity mathematical models of those robots. For this purpose, dynamic models are built from experimental data. The main objective of this thesis is thus to provide robotic engineers with automatic tools for identifying dynamic models of industrial robot arms. To achieve this aim, a comparative analysis of the existing methods dealing with robot identification is made. That allows discerning the advantages and the limitations of each method. From those observations, contributions are presented on three axes. First, the study focuses on the estimation of the joint velocities and accelerations from the measured position, which is required for the model construction. The usual method is based on a home-made prefiltering process that needs a reliable knowledge of the system’s bandwidths, whereas the system is still unknown. To overcome this dilemma, we propose a method able to estimate the joint derivatives automatically, without any setting from the user. The second axis is dedicated to the identification of the controller. For the vast majority of the method its knowledge is indeed required. Unfortunately, for copyright reasons, that is not always available to the user. To deal with this issue, two methods are suggested. Their basic philosophy is to identify the control law in a first step before identifying the dynamic model of the robot in a second one. The first method consists in identifying the control law in a parametric way, whereas the second one relies on a non-parametric identification. Finally, the third axis deals with the home-made setting of the decimate filter. The identification of the noise filter is introduced similarly to methods developed in the system identification community. This allows estimating automatically the dynamic parameters with low covariance and it brings some information about the noise circulation through the closed-loop system. All the proposed methodologies are validated on an industrial robot with 6 degrees of freedom. Perspectives are outlined for future developments on robotic systems identification and other complex problems.

L’industrie moderne fait largement appel à des robots industriels afin de réduire les coûts, ou encore améliorer la productivité et la qualité par exemple. Pour ce faire, une haute précision et une grande vitesse sont simultanément nécessaires. La conception de lois de commande conformes à de telles exigences demande une modélisation mathématique précise de ces robots. A cette fin, des modèles dynamiques sont construits à partir de données expérimentales. L’objectif de cette thèse est ainsi de fournir aux ingénieurs roboticiens des outils automatiques pour l’identification de bras robotiques. Dans cette perspective, une analyse comparative des méthodes existantes pour l’identification de robot est réalisée. Les avantages et inconvénients de chaque méthode sont ainsi mis en exergue. À partir de ces observations, les contributions sont articulées selon trois axes. Premièrement, l’étude porte sur l’estimation des vitesses et accélérations des corps du robot à partir de la position mesurée. Ces informations sont en effet nécessaires à la construction du modèle. La méthode usuelle est basée sur prétraitement "sur mesure" qui requière une connaissance fiable des bande-passantes du système, alors que celui-ci est encore inconnu. Pour surmonter ce dilemme, nous proposons une méthode capable d’estimer les dérivées automatiquement sans réglage préalable par l’utilisateur. Le deuxième axe concerne l’identification du contrôleur. Sa connaissance est en effet requise par la grande majorité des méthodes d’identification. Malheureusement, pour des raisons de propriété industrielle, il n’est pas toujours accessible. Pour traiter ce problème, deux méthodes sont introduites. Leur principe de base est d’identifier la loi de commande dans un premier temps avant d’identifier le modèle dynamique du bras robotique dans un second temps. La première méthode consiste à identifier la loi de commande de manière paramétrique, alors que la seconde fait appel à une identification non-paramétrique. Finalement, le troisième axe porte sur le réglage "sur mesure" du filtre decimate. L’identification du filtre de bruit est introduite en s’inspirant des méthodes développées par la communauté d’identification de systèmes. Ceci permet l’estimation automatique des paramètres dynamiques avec de faibles covariances tout en apportant une connaissance concernant la circulation du bruit à travers le système en boucle-fermée. Toutes les méthodes proposées sont validées sur un robot industriel à six degrés de liberté. Des perspectives sont esquissées pour de futurs travaux portant sur l’identification de systèmes robotiques, voire d’autres applications. / In modern manufacturing, industrial robots are essential components that allow saving cost, increase quality and productivity for instance. To achieve such goals, high accuracy and speed are simultaneously required. The design of control laws compliant with such requirements demands high-fidelity mathematical models of those robots. For this purpose, dynamic models are built from experimental data. The main objective of this thesis is thus to provide robotic engineers with automatic tools for identifying dynamic models of industrial robot arms. To achieve this aim, a comparative analysis of the existing methods dealing with robot identification is made. That allows discerning the advantages and the limitations of each method. From those observations, contributions are presented on three axes. First, the study focuses on the estimation of the joint velocities and accelerations from the measured position, which is required for the model construction. The usual method is based on a home-made prefiltering process that needs a reliable knowledge of the system’s bandwidths, whereas the system is still unknown. To overcome this dilemma, we propose a method able to estimate the joint derivatives automatically, without any setting from the user. The second axis is dedicated to the identification of the controller. For the vast majority of the method its knowledge is indeed required. Unfortunately, for copyright reasons, that is not always available to the user. To deal with this issue, two methods are suggested. Their basic philosophy is to identify the control law in a first step before identifying the dynamic model of the robot in a second one. The first method consists in identifying the control law in a parametric way, whereas the second one relies on a non-parametric identification. Finally, the third axis deals with the home-made setting of the decimate filter. The identification of the noise filter is introduced similarly to methods developed in the system identification community. This allows estimating automatically the dynamic parameters with low covariance and it brings some information about the noise circulation through the closed-loop system. All the proposed methodologies are validated on an industrial robot with 6 degrees of freedom. Perspectives are outlined for future developments on robotic systems identification and other complex problems.