Apprentissage du contrôle de systèmes complexes par l'auto-organisation coopérative d'un système multi-agent: Application à la calibration de moteurs à combustion

This thesis tackles the problem of complex systems control with a multi-agent approach. Controlling a system means applying the adequate actions on its inputs, in order to put the system in a desired state. Usual methods are based on analytical models of the controlled system. They find their limits with complex systems, because of the non-linear dynamics. Building a model of this kind of system is indeed very difficult, and exploiting such a model is even harder. A better approach is to learn how to control, without having to exploit any model. But Ashby's Law taught us that the controller must be at least as complex as the controlled system. A part of the challenge is to build a complex system with the correct functionnality.This challenge is tackled with the Adaptive Multi-Agent Systems (AMAS) approach, which relies on cooperation and emergence to design adaptive multi-agent systems able to perform complex tasks.Cette thèse s'intéresse au contrôle de systèmes complexes, et propose une solution multi-agent.Contrôler un système, c'est appliquer les modifications adéquates sur ses entrées de façon à placer ses sorties dans un état attendu. Les méthodes habituelles se basent majoritairement sur l'utilisation de modèles mathématiques du système contrôlé, afin de calculer les actions de contrôle à effectuer. Ces méthodes trouvent leurs limites face aux systèmes complexes, qui ont une dynamique non-linéaire, et sont souvent bruités et instables. La construction d'un modèle est dans ce cas une tâche ardue, qui peut s'étendre sur plusieurs années. La plupart des méthodes proposent alors d'utiliser un algorithme d'apprentissage artificiel pour apprendre un modèle. Cependant, le modèle produit demeure difficile à exploiter pour le contrôle, puisqu'il reproduit les caractéristiques difficiles du système réel, notamment sa non-linéarité. Une meilleure approche, adoptée dans cette thèse, consiste à apprendre directement le contrôle. La loi de la variété requise indique que, pour être capable d'accomplir sa tâche, le contrôleur doit être au moins aussi complexe que le système contrôlé. Il faut donc concevoir un système capable d'apprendre, de contrôler, et surtout, de franchir le mur de la complexité.La distribution du contrôle, c'est-à-dire l'affectation du contrôle de chaque entrée d'un système à des contrôleurs plus ou moins indépendants, permet de s'attaquer à la complexité. Mais cela demeure un sujet de recherche actif, à plus forte raison lorsque vient s'ajouter une problématique d'apprentissage. Les systèmes multi-agents (SMA), composés d'entités autonomes, se prêtent naturellement aux problèmes distribués et peuvent ainsi beaucoup apporter. En particulier, les systèmes multi-agents adaptatifs (AMAS) s'appuient sur l'auto-organisation des agents pour faire émerger une fonction globale adéquate. Cette auto-organisation est guidée par la coopération. Chaque agent est capable de détecter et de résoudre les situations dans lesquelles il ne peut accomplir sa tâche. Un AMAS est ainsi doté de fortes capacités d'adaptation et d'apprentissage. Il est également capable, grâce à l'émergence, d'accomplir des tâches complexes. Appliquée au problème du contrôle et de son apprentissage, cette approche conduit à la définition d'un SMA particulier, présenté dans cette thèse. Les expérimentations, menées sur des simulations ainsi qu'en situation réelle (sur un moteur à combustion), ont montré la capacité du système à apprendre le contrôle de plusieurs entrées en fonction de critères sur plusieurs sorties, tout en étant robuste aux perturbations, et facile à instancier. Ces résultats sont analysés pour conclure sur la validité du système

Model-free Optimization of an Engine Control Unit thanks to Self-Adaptive Multi-Agent Systems

International audienceControlling complex systems, such as combustion engines, imposes to deal with high dynamics, non-linearity and multiple interdependencies. To handle these difficulties we can either build analytic models of the process to control, or enable the controller to learn how the process behaves. Tuning an engine control unit (ECU) is a complex task that demands several months of work. It requires a lot of tests, as the optimization problem is non-linear. Efforts are made by researchers and engineers to improve the development methods, and find quicker ways to perform the calibration. Adaptive Multi-Agent Systems (AMAS) are able to learn and adapt themselves to their environment thanks to the cooperative self organization of their agents. A change in the organization of the agents results in a change of the emergent function. Thus we assume that AMAS are a good alternative for complex systems control. In this paper, we describe a multi-agent control system that was used to perform the automatic calibration of an ECU. Indeed, the problem of calibration is very similar to the one of control: finding the adequate values for a system to perform optimally

Controlling Complex Systems Dynamics without Prior Model

International audienceControlling complex systems imposes to deal with high dynamics, non-linearity and multiple interdependencies. To handle these dif¿culties we can either build analytic models of the process to control, or enable the controller to learn how the process behaves. Adaptive Multi-Agent Systems (AMAS) are able to learn and adapt themselves to their environment thanks to the cooperative self-organization of their agents. A change in the organization of the agents results in a change of the emergent function. Thus we assume that AMAS are a good alternative for complex systems control, reuniting learning, adaptivity, robustness and genericity. The problem of control leads to a speci¿c architecture presented in this paper

Self-Organizing Multi-Agent Systems for the Control of Complex Systems

Because of the law of requisite variety, designing a controller for complex systems implies designing a complex system. In software engineering, usual top-down approaches become inadequate to design such systems. The Adaptive Multi-Agent Systems (AMAS) approach relies on the cooperative self-organization of autonomous micro-level agents to tackle macro-level complexity. This bottom-up approach provides adaptive, scalable, and robust systems. This paper presents a complex system controller that has been designed following this approach, and shows results obtained with the automatic tuning of a real internal combustion engine

The Self-Adaptive Context Learning Pattern: Overview and Proposal

International audienceOver the years, our research group has designed and developed many self-adaptive multi-agent systems to tackle real-world complex problems, such as robot control and heat engine optimization. A recurrent key feature of these systems is the ability to learn how to handle the context they are plunged in, in other words to map the current state of their perceptions to actions and effects. This paper presents the pattern enabling the dynamic and interactive learning of the mapping between context and actions by our multi-agent systems

Self-Organizing Agents for an Adaptive Control of Heat Engines

Controlling heat engines imposes to deal with high dynamics, non-linearity and multiple interdependencies. A way handle these difficulties is enable the controller to learn how the engine behaves, hence avoiding the costly use of an explicit model of the process. Adaptive Multi-Agent Systems (AMAS) are able to learn and to adapt themselves to their environment thanks to the cooperative self-organization of their agents. A change in the organization of the agents results in a change of the emergent function. Thus we assume that AMAS are a good alternative for complex systems control, reuniting learning, adaptivity, robustness and genericity. In this paper, we present an AMAS for the control of heat engines and show several results

Principles and Experiments of a Multi-Agent Approach for Large Co-Simulation Networks Initialization

ICAART 2017 will be held in conjunction with ICORES 2017 and ICPRAM 2017.International audienceSimulating large systems, such as smart grids, often requires to build a network of specific simulators. Makingheterogeneous simulators work together is a challenge in itself, but recent advances in the field of co-simulationare providing answers. However, one key problem arises, and has not been sufficiently addressed: the initial-ization of such networks. Many simulators need to have proper input values to start. But in the network, eachinput is another simulator’s output. One have to find the initial input values of all simulators so their computedoutput is equal to the initial input value of the other linked simulators. Given that simulators often containdifferential equations, this is hard to solve even with a small number of simulators, and nearly impossible witha large number of them. In this paper, we present a mutli-agent system designed to solve the co-simulationinitialization problem, and show preliminary results on large networks

Mimicking Complexity : Automatic Generation of Models for the Development of Self-Adaptive Systems

Many methods for complex systems control use a black box approach where the internal states and mechanisms of the controlled process are not needed to be known. Usually, such systems are tested on simulations before their validation on the real world process they were made for. These simulations are based on sharp analytical models of the target process that can be very difficult to obtain. But is it useful in the case of black box methods? Since the control system only sees inputs and outputs and is able to learn, we only need to mimic the typical features of the process (such as non-linearity, interdependencies, etc) in an abstract way. This paper aims to show how a simple and versatile simulator can help the design of systems that have to deal with complexity. We present a generator of models used in the simulator and discuss the results obtained in the case of the design of a control system for heat engines

From Smart Campus to Smart Cities: Issues of the Smart Revolution

International audienceWith 7/10 of the population which will live in urban center by 2050, Cities face huge challenge inviting to rethink the very concept of a City. The Smart Cities concept proposes to use Information and Communication Technologies to design sustainable solutions for improving socio-ecological aspects of cities. In this paper, we present the challenge of designing IT applications in Smart Cities and present our own attempt to transform our university into a Smart Campus. Through examples of use cases, we discuss how the Smart Cities concept intends to put citizen back at the center of the city and highlights that inter-disciplinary work is mandatory to address the challenges of Smart Cities