Dynamique des systèmes

Résumé

La dynamique des systèmes fait partie de la théorie des systèmes. C'est une approche pour comprendre le comportement des systèmes complexes dans le temps en les représentant par des systèmes dynamiques. Elle prend en compte les boucles de rétroaction internes et les effets retard qui affectent le comportement global du système. Elle est fondée sur des modèles qui sont une formalisation de nos suppositions à propos d’un système (Hall and Day, 1977). En dynamique des systèmes, faire tourner une simulation consiste à résoudre les équations mathématiques pour obtenir la valeur de chaque variable au cours du temps. Les équations contiennent des paramètres qu’il faut souvent calibrer sur des données historiques. La sortie d’une simulation pour un jeu de données d’entrée fixé s’appelle un scénario. Principes La dynamique des systèmes est une technique de modélisation mathématique qui permet de comprendre et d'analyser des problèmes complexes. Elle a été conçue dans les années 1950

Source officielle

https://fr.wikipedia.org/wiki/Dynamique_des_syst%C3%A8mes

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Impact of Incentives on the Adoption of Solar Photovoltaic Panels in Geneva and Amsterdam: A case study using system dynamics

Antoine El Hani

2020

Chargement

On the use of input-output feedback linearization techniques for the control of nonminimum-phase systems

Kahina Guemghar

The objective of the thesis is to study the possibility of using input-output feedback linearization techniques for controlling nonlinear nonminimum-phase systems. Two methods are developed. The first one is based on an approximate input-output feedback linearization, where a part of the internal dynamics is neglected, while the second method focuses on the stabilization of the internal dynamics. The inverse of a nonminimum-phase system being unstable, standard input-output feedback linearization is not effective to control such systems. In this work, a control scheme is developed, based on an approximate input-output feedback linearization method, where the observability normal form is used in conjunction with input-output feedback linearization. The system is feedback linearized upon neglecting a part of the system dynamics, with the neglected part being considered as a perturbation. Stability analysis is provided based on the vanishing perturbation theory. However, this approximate input-output feedback linearization is only effective for very small values of the perturbation. In the general case, the internal dynamics cannot be crushed and need to be stabilized. On the other hand, predictive control is an effective approach for tackling problems with nonlinear dynamics, especially when analytical computation of the control law is difficult. Therefore, a cascade-control scheme that combines input-output feedback linearization and predictive control is proposed. Therein, inputoutput feedback linearization forms the inner loop that compensates the nonlinearities in the input-output behavior, and predictive control forms the outer loop that is used to stabilize the internal dynamics. With this scheme, predictive control is implemented at a re-optimization rate determined by the internal dynamics rather than the system dynamics, which is particularly advantageous when internal dynamics are slower than the input-output behavior of the controlled system. Exponential stability of the cascade-control scheme is provided using singular perturbation theory. Finally, both the approximate input-output feedback linearization and the cascade-control scheme are implemented successfully, on a polar pendulum 'pendubot' that is available at the Laboratoire d'Automatique of EPFL. The pendubot exhibits all the properties that suit the control methodologies mentioned above. From the approximate input-output feedback linearization point of view, the pendubot is a nonlinear system, not input-state feedback linearizable. Also, the pendubot is nonminimum phase, which prevents the use of standard input-output feedback linearization. From the cascade control point of view, although the pendubot has fast dynamics, the input-output feedback linearization separates the input-output system behavior from the internal dynamics, thus leading to a two-time-scale systems: fast input-output behavior, which is controlled using a linear controller, and slow reduced internal dynamics, which are stabilized using model predictive control. Therefore, the cascade-control scheme is effective, and model predictive control can be implemented at a low frequency compared to the input-output behavior.

EPFL2005

Chargement

Policymaking is a complex process that has been studied using policy process theories almost exclusively. These theories have been built using a large number of qualitative cases. Such methods are useful for theory building but remain limited for theory exploration and policy advice. On a different front, socio-technical system (STS) simulations are often used to test the impact and effectiveness of policies. This is done by using policy scenarios. This manner of dealing with deep uncertainty disregards the dynamic aspect of the policy process and of the way STS interact with policies. The objective of this dissertation is to present an approach that can be used to systematically model and simulate the policy process. This dissertation demonstrates how such a model can be used in these two widely different applications to explore the policy process theories and to better account for deep uncertainty in STS simulations. In the first part of the dissertation, I present a common language. It considers four building blocks using concepts from prominent theories as requirements to any model of the policy process: time, the policy arena, actors and the environment, and the actor interactions. Additionally, in this part, I argue why agent-based modelling is best suited to simulate the policy process. Finally, I present the hybrid modelling approach that is used to incorporate the policy process model into already existing STS models. The second part focuses on the simplest implementation model. This model is the first use of the common language and is entitled simplest implementation as the common language is used to build the simplest model possible capable of emulating the policy process. It is created to demonstrate the potential of the common language and is tested with three different STS. A predation model is used to present the possibilities stemming from this simplest implementation model. It is then added to a model of the Swiss electricity market to demonstrate what benefits the endogenisation of the policy process can have on the study of a complex STS. As a third example, the policy process model is added to a system dynamics model of flood safety in The Netherlands to demonstrate its versatility. The third part of this dissertation consists of the advocacy coalition framework (ACF) implementation model. In this part, I present a model, also developed using the common language, that is aimed at emulating the ACF as closely as possible. The ACF being a complex framework, this implementation is done in steps of increasing complexity. The first step introduces policy learning to the model, the second introduces coalitions, the third and fourth steps introduce aspects of partial knowledge and imperfect information. This ACF implementation model is also demonstrated using first a predation model and second a model of the Swiss electricity market. In conclusion, this dissertation shows that it is possible to model the policy process. However it was shown that the policy process theories could benefit from additional and more focused studies so that behaviours and mechanisms can be better understood. This work can be the first step to such studies. It was also shown that introducing the policy process in a STS simulation can be useful to better understand said system. This approach allows for an easy integration to already existing models. Finally, this work can be extended by looking at other theories or different applications.

EPFL2021

Chargement

EPFL2021

On the use of input-output feedback linearization techniques for the control of nonminimum-phase systems

Kahina Guemghar

EPFL2005