Développement d'une plateforme générique de conception de commande prédictive non linéaire : application à l'estimation de la teneur en eau des particules dans un séchoir à lit fluidisé

Ce projet de maîtrise s'effectue en collaboration avec un partenaire industriel, soit le groupe process analytical support group chez Pzer Global Manufacturing Services. L'objectif général de ce partenariat est d'accroître le niveau d'automatisation dans la chaîne de fabrication de l'industrie pharmaceutique. Notamment, des améliorations sont possibles dans l'instrumentation des équipements ainsi que dans l'asservissement de ceux-ci par des stratégies d'optimisation en temps réel. Cet ouvrage s'attaque spécifiquement au cas du séchoir à lit fluidisé. Ce procédé est décrit par une dynamique non linéaire si bien que les outils disponibles pour concevoir une commande prédictive couplée d'un observateur n'offrent pas présentement la flexibilité requise pour répondre à l'objectif du projet. La programmation d'une application de type interface graphique permet à son utilisateur de concevoir ces stratégies pour le procédé de son choix. L'application a été validée en concevant cinq observateurs non linéaires pour le séchage de granules pharmaceutiques. L'objectif est de pouvoir fournir une mesure de teneur en eau ande remplacer la sonde proche infrarouge actuellement utilisée. Il a été montré que certains observateurs sont en mesure de reproduire précisément les données expérimentales tout en montrant un effort de calcul plus acceptable. Ainsi, l'application permet de déployer facilement des solutions efficaces et mesurables pour l'asservissement de procédés pharmaceutiques,dont l'instrumentation du séchoir à lit fluidisé est un exemple spécique. Le déploiement au sein du groupe de recherche de l'application et son amélioration fait partie des travaux futurs.This work is in partnership with the process analytical support group at Pzer Global Manu-facturing Services. The objective is to increase the level of automation in the pharmaceutical manufacturing process. Possible improvements lie in the instrumentation and control of the equipment. This work particularly aims the discontinuous fluid bed drying (FBD) case. A nonlinear dynamic describes the FBD operation as well as many other pharmaceutical process. Actual predictive control conception tools are not flexible enough. Hence, the programming of a graphical user interface software allows the user to design a control algorithm for nonlinear processes. Validation is ensured by generating five nonlinear estimators to monitor the drying. One of these, based on the moving horizon estimator, could replace the near-infrared probe on the fluid bed dryer as a moisture content soft sensor. Results show that the estimator reproduce accurately the pilot scale experimental data with a moderate computational effort. Thus, the software allows to easily implement control and observation strategies for the pharmaceutical industry Future works imply the deployment and the improvement of the software

Bio­-inspired approaches to the control and modelling of an anthropomimetic robot

Introducing robots into human environments requires them to handle settings designed specifically for human size and morphology, however, large, conventional humanoid robots with stiff, high powered joint actuators pose a significant danger to humans. By contrast, “anthropomimetic” robots mimic both human morphology and internal structure; skeleton, muscles, compliance and high redundancy. Although far safer, their resultant compliant structure presents a formidable challenge to conventional control. Here we review, and seek to address, characteristic control issues of this class of robot, whilst exploiting their biomimetic nature by drawing upon biological motor control research. We derive a novel learning controller for discovering effective reaching actions created through sustained activation of one or more muscle synergies, an approach which draws upon strong, recent evidence from animal and humans studies, but is almost unexplored to date in musculoskeletal robot literature. Since the best synergies for a given robot will be unknown, we derive a deliberately simple reinforcement learning approach intended to allow their emergence, in particular those patterns which aid linearization of control. We also draw upon optimal control theories to encourage the emergence of smoother movement by incorporating signal dependent noise and trial repetition. In addition, we argue the utility of developing a detailed dynamic model of a complete robot and present a stable, physics-­‐‑based model, of the anthropomimetic ECCERobot, running in real time with 55 muscles and 88 degrees of freedom. Using the model, we find that effective reaching actions can be learned which employ only two sequential motor co-­‐‑activation patterns, each controlled by just a single common driving signal. Factor analysis shows the emergent muscle co-­‐‑activations can be reconstructed to significant accuracy using weighted combinations of only 13 common fragments, labelled “candidate synergies”. Using these synergies as drivable units the same controller learns the same task both faster and better, however, other reaching tasks perform less well, proportional to dissimilarity; we therefore propose that modifications enabling emergence of a more generic set of synergies are required. Finally, we propose a continuous controller for the robot, based on model predictive control, incorporating our model as a predictive component for state estimation, delay-­‐‑ compensation and planning, including merging of the robot and sensed environment into a single model. We test the delay compensation mechanism by controlling a second copy of the model acting as a proxy for the real robot, finding that performance is significantly improved if a precise degree of compensation is applied and show how rapidly an un-­‐‑compensated controller fails as the model accuracy degrades