During fast point-to-point motion, the inherent joint flexibility could be detrimental in terms of residual vibration. Aiming to minimize the vibration, the command shaping method has been developed so as to remove critical energy from the input profile at resonant frequencies. Since this method requires information of a physical model in order to find the target frequencies, the quality of the shaped command profile relies on the accuracy of the model parameter estimation. Therefore, in this work, a system identification method using Instrumental Variables is applied from the literature. Compared with the classic Ordinary Least Square method, the IV approach has successfully improved the estimation of parameters, based on simulation results. The accuracy of parameter estimation influences the command profile, as does the feedback controller. In this work, starting from a mathematical derivation with a mismatch model due to a feedback controller called Computed Torque Control, insight for the closed-loop system is given with regard to the interaction between control gains and the actual resonant frequencies. It is found that the control gain is able to modify the actual resonant frequency curve, and push it into or out of the shaping bounds which are generated from the command shaping method. Further analysis based on the simulation results shows that the overlap area between the shaping bounds and the actual frequencies affects the level of residual vibration. In light of this fact, an optimal control gain exists and is found when the estimation error is in a certain range. At the end, recommendations for choosing the control gains are provided.

Bien que le traitement conjoint des mesures multimodales soit supposé conduire à de meilleures performances que celles obtenues en exploitant une seule modalité ou plusieurs modalités indépendamment, il existe des exemples en littérature qui prouvent que c'est pas toujours vrai. Dans cette thèse, nous analysons rigoureusement, en termes d'information mutuelle et d'erreur d'estimation, les différentes situations de l'analyse multimodale afin de déterminer les conditions conduisant à des performances optimales.Dans la première partie, nous considérons le cas simple de deux ou trois modalités, chacune étant associée à la mesure bruitée d'un signal, avec des liens entre modalités matérialisés par les corrélations entre les parties utiles du signal et par les corrélations les bruits. Nous montrons comment les performances obtenues sont améliorées avec l'exploitation des liens entre les modalités. Dans la seconde partie, nous étudions l'impact sur les performances d'erreurs sur les liens entre modalités. Nous montrons que ces fausses hypothèses dégradent les performances, qui peuvent alors devenir inférieure à celles atteintes avec une seule modalité.Dans le cas général, nous modélisons les multiples modalités comme un canal gaussien bruité. Nous étendons alors des résultats de la littérature en considérant l'impact d'erreurs sur les densités de probabilité du signal et du bruit sur l'information transmise par le canal. Nous analysons ensuite cette relation dans la cas d'un modèle simple de deux modalités. Nos résultats montrent en particulier le fait inattendu qu'une double inadéquation du bruit et du signal peuvent parfois se compenser et ainsi conduire à de très bonnes performances. / The joint processing of multimodal measurements is supposed to lead to better performances than those obtained using a single modality or several modalities independently. However, in literature, there are examples that show that is not always true. In this thesis, we analyze, in terms of mutual information and estimation error, the different situations of multimodal analysis in order to determine the conditions to achieve the optimal performances.In the first part, we consider the simple case of two or three modalities, each associated with noisy measurement of a signal. These modalities are linked through the correlations between the useful parts of the signal and the correlations between the noises. We show that the performances are improved if the links between the modalities are exploited. In the second part, we study the impact on performance of wrong links between modalities. We show that these false assumptions decline the performance, which can become lower than the performance achieved using a single modality.In the general case, we model the multiple modalities as a noisy Gaussian channel. We then extend literature results by considering the impact of the errors on signal and noise probability densities on the information transmitted by the channel. We then analyze this relationship in the case of a simple model of two modalities. Our results show in particular the unexpected fact that a double mismatch of the noise and the signal can sometimes compensate for each other, and thus lead to very good performances.