Regelungstechnik

Mobile robots have been utilized and have assisted humans for decades. In recent years, even groups of robots have been used for specific tasks that require the control of each robot and the distribution of commands. Using groups of robots generally requires complex control structures to ensure the safety and performance of groups completing a mission. This work presents control structures for tasks such as source localization, formation control, position tracking, collision avoidance, and synthesis of an according controller. The stability of the system is examined. The mission objective of source localization and itsextension to global source localization is introduced and tackled using different algorithms. The work concludes with the implementation of a robot platform along with simulation and experimental results to show the performance of the presented methods. The main findings include the proposal of a synthesis problem for source localization with formation control, a methodology that allows the calculation of the maximum permitted delay in a multi-robot system, and implementation for source localization in an unknown indoor environment.

This paper presents a criterion and control scheme based on the assumption of a bound on the gradient of a field distribution which guarantees to find the global extremum of the distribution. Mobile robots move through the search space gathering information at points which are calculated as a minimization problem over part of the search space which is guaranteed to include the global extremum based on the previously gathered measurements. Position control in combination with collision avoidance drives each robot to the next position while communicating its position to the other robots. Upon arrival, the next measurement of the field distribution is performed and the next position reference is calculated by each robot until the robots narrowed the search area to a single location. Previously proposed control schemes can find single points as candidates for the global maximum but struggle to guarantee that this point is the global extremum. Simulation results with robot models show the performance in comparison to a naive approach.

Formation control has been one of the important topics covered in the researches on the multi-agent systems. The applications of the multi-agent systems are significant in variety of tasks such as search and rescue missions, forest fire detection, reconnaissance, surveillance, etc. Inspired by the cooperative ability as well as the intelligence of natural animal groups such as schools of fishes, flocks of birds, swarm of ants, etc., this dissertation develops the artificial vector field method for formation control of autonomous robots while tracking one or more moving targets in a dynamic environment. In our approach, the proposed artificial vector fields, which consist of the attractive, repulsive, and rotational force field, are combined with the damping term in the formation control laws in order to control the velocity, heading, connectivity, as well as the obstacle avoidance of a swarm of autonomous robots while in motion. Using this approach, autonomous robots are not only controlled to move along a desired trajectory towards the target, but are also held in a specified formation without collisions during movement. In other words, under the effects of the proposed artificial vector fields, the member robots of a swarm will move together in a specified formation with the velocity matching, without collisions among them while tracking the target. In addition, the free robots will themselves approach the created formation from their swarm in order to obtain the fixed position in this formation. Especially, the thesis then explains that by using the proposed hybrid force field in the obstacle avoiding controller, the local minima problems that still exist in the traditional potential field method (for example, when a robot is trapped in U-shape obstacle, etc.) will be solved. In the proposed hybrid force field, the local repulsive force field surrounding obstacles, which is stronger when the robot is closer to the obstacles, is utilized to repel the robot away from the obstacles, while a local rotational force field is added to surround the obstacles in order to drive robot to escape the obstacles in the direction of the target’s trajectory. Therefore, robots can easily and quickly avoid obstacles, as well as escape complex obstacles along their moving trajectory in order to complete the assigned tasks with their swarm. The thesis focuses on two main issues in formation control, namely, (i) formation control following the desired formations and (ii) cooperative formation control. The first issue concerns how robots are controlled by the proposed formation control algorithm in order to approach the coordinated virtual nodes in the desired formation (for example, Vshape, line or circular shape), and to maintain following these virtual nodes during tracking; while the second issue showcases the use of the proposed cooperative formation control law, where robots will automatically cooperate with each other in their neighboring relationship in order to generate and maintain the cohesion in their formation.

Die vorliegende Arbeit soll einen Beitrag zum effizienten Betrieb von PEM-Brennstoffzellensystemen im Hinblick auf deren Regelung leisten. Beschrieben wird die Modellierung als Grundlage der Leistungs- und kathodenseitigen Druckregelung. Die Modellbildung basiert auf chemischen, strömungstechnischen, geometrischen und elektrischen Zusammenhängen. Das elektrische Modell und die Strömungsübergänge am verwendeten Ventil zur kathodenseitigen Nachdruckregelung sowie dessen Ventilcharakteristik sind stark nichtlinear. Anwendung findet daher die Nichtlineare Modellprädiktive Regelung für den kontinuierlichen Betrieb der Brennstoffzelle zur elektrischen Leistungsbereitstellung. Für die aus verschiedenen Gründen auftretenden Modellungenauigkeiten wird die Modellprädiktive Regelungsstrategie um eine Modellkorrektur ergänzt, sodass stationäre Genauigkeit während verschiedener Belastungsszenarien sichergestellt ist. Als Grundlage der Modellkorrektur wird ein Erweitertes Kalman-Filter eingesetzt. Für die anodenseitige Druckregelung wird während der regelmäßigen Spülvorgänge die Iterativ Lernende Regelung eingesetzt. Der Wasserstoffdruck soll während der Spülvorgänge, die dem Entfernen von angesammeltem Wasserkondensat auf den Reaktionsflächen und Stickstoff im System dienen, konstant bleiben, um einerseits den Druckunterschied zwischen Anoden- und Kathodenvolumen zu begrenzen sowie andererseits die positive Auswirkung des konstanten Drucks während der Spülvorgänge zu nutzen. Der Vorgang kann schneller und im Hinblick auf ein exakt zu extrahierendes Volumen je Spülvorgang präziser durchgeführt werden. Der Aufbau verschiedener Lernfilter und die Anwendung der klassischen Iterativ Lernenden Regelung sowie der Optimierend Iterativ Lernenden Regelung werden gezeigt. Die verschiedenen Regelungsstrategien werden an einem Brennstoffzellensystem mit einer elektrischen Spitzenleistung von 4,4?kW umgesetzt.