Use of Transient Measurements for Static Real-Time Optimization

Modifier adaptation (MA) is a real-time optimization (RTO) method characterized by its ability to enforce plant optimality upon convergence despite the presence of model uncertainty. The approach is based on correcting the available model using gradient estimates computed at each iteration. MA uses steady-state measurements and solves a static optimization problem. Hence, after every input change, one typically waits for the plant to reach steady state before measurements are taken. With many iterations, this can make convergence to the plant optimum rather slow. Recently, an approach that uses transient measurements for steady-state MA has been proposed. This way, plant optimality can be reached in a single transient operation. This paper proposes to improve this approach by using a dynamic model to process transient measurements for gradient computations. The approach is illustrated through the simulated example of a CSTR. Furthermore, the proposed method is less dependent on the choice of the RTO period. The time needed to reach plant optimality is of the order of the plant settling time, whereas several transitions to steady state would have been necessary using the standard static MA scheme

Qualitative visual servoing for navigation

We propose in this article a novel approach for vision-based control of a robotic system during a navigation task. This technique is based on a topological representation of the environment in which the scene is directly described within the sensor space, by an image database acquired off-line. Before each navigation task, a preliminary step consists in localizing the current position of the robotic system. This is realized through an image retrieval scheme, by searching within the database the views that are the most similar to the one given by the camera. Then a classical shortest path finding algorithm enables to extract from the database a sequence of views that visually describe the environment the robot has to go through in order to reach the desired position. This article mainly focuses on the control law that is used for controlling the motions of the robotic system, by comparing the visual information extracted from the current view and from the image path. This control law does not need a CAD model of the environment, and does not perform a temporal path planning. Furthermore, the images from the path are not considered as successive desired positions that have to be consecutively reached by the camera. The qualitative visual servoing scheme proposed, based on cost functions, ensures that the robotic system is always able to observe some visual features initially detected on the image path. Experiments realized in simulation and with a real system demonstrate that this formalism enables to control a camera moving in a 3D environment.Dans cet article, une nouvelle méthode est proposée pour contrôler les mouvements d'un système robotique à l'aide d'un capteur de vision monoculaire durant une tâche de navigation. Cette approche s'appuie sur une représentation topologique de l'environnement, où la scène est directement décrite dans l'espace du capteur par une base d'images acquises hors-ligne. Lors de la navigation, une étape préalable de recherche d'images permet de localiser la position courante du robot, en mettant en relation la vue que sa caméra fournit avec celles stockées dans la base. Un algorithme classique de recherche de plus-court chemin permet alors d'extraire de la base un ensemble de vues caractérisant l'espace à parcourir afin de rejoindre la position désirée. Cet article se concentre principalement sur la loi de commande permettant de déduire les mouvements du robot en fonction des informations extraites de ce chemin et de la vue courante de la caméra. Notre méthode ne s'appuie pas sur un modèle 3D de la scène, et n'effectue pas une planification temporelle de la trajectoire à réaliser. De plus, les images du chemin ne sont pas considérées comme des positions désirées intermédiaires vers lesquelles doit converger la caméra. Le schéma d'asservissement visuel proposé, qualifié de qualitatif, repose sur des fonctions de coût, et assure que le robot peut toujours observer les amers visuels initialement détectés sur le chemin d'images. Des expériences réalisées en simulation et avec un système réel montrent que le formalisme proposé permet de contrôler les mouvements d'une caméra dans un environnement 3D

Numerical Flow Analysis of the GAMM Turbine at nominal and off-design Operating Conditions

The flow in a Francis turbine runner (GAMM Turbine) is analysed numerically. Different operating points are calculated using two industrial software packages based respectively on a finite element method (N3S) and a finite volume method (TASCflow®) and compared to experimental results. The numerical results allow to observe physical phenomena in the runner that are important in the process of hydraulic turbomachinery design. Values of Cu and Cm velocity components, blade pressure distribution and recirculation in the flow are compared to experimental results at nominal and off-design flow conditions. The experimental and numerical results show a similar efficiency evolution in function of flow rate and head, however the absolute level of energetic losses are overestimated by the two numerical codes