Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING / Includes bibliographical references. / by David Lui. / B.S.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaves 328-332. / by Marcel Coderch i Collell. / Ph.D.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Includes bibliographical references. / by Pierre Dersin. / Ph.D.

This thesis deals with the design of multivariable controllers for stable linear time-invariant multi-input multi-output systems, with an unknown mathematical model, subject to constant reference/disturbance signals and actuator saturation constraints. A new controller parameter optimization approach, which can be carried out experimentally with no knowledge of the plant model nor of the order of the system, is proposed. The approach has the advantage that controllers can be optimized by perturbing only the initial conditions of the servocompensator, and that the order of the resulting controller obtained can be specified by the designer. Implementation of the proposed controller design approach is described, and an experimental application study of the proposed method applied to a multivariable system with industrial sensor/actuator components is presented to illustrate the feasibility of the design method in an industrial environment.

This thesis deals with the design of multivariable controllers for stable linear time-invariant multi-input multi-output systems, with an unknown mathematical model, subject to constant reference/disturbance signals and actuator saturation constraints. A new controller parameter optimization approach, which can be carried out experimentally with no knowledge of the plant model nor of the order of the system, is proposed. The approach has the advantage that controllers can be optimized by perturbing only the initial conditions of the servocompensator, and that the order of the resulting controller obtained can be specified by the designer. Implementation of the proposed controller design approach is described, and an experimental application study of the proposed method applied to a multivariable system with industrial sensor/actuator components is presented to illustrate the feasibility of the design method in an industrial environment.

This thesis presents decentralized model reference adaptive control techniques for systems with full-state feedback and systems with output feedback. The controllers are strictly decentralized, that is, each local controller uses feedback from only local subsystems and no information is shared between local controllers. The full-state feedback decentralized controller is effective for multi-input systems, where the dynamics matrix and control-input matrix are unknown. The decentralized controller achieves asymptotic stabilization and command following in the presence of sinusoidal disturbances with known spectrum. We present a construction technique of the reference-model dynamics such that the decentralized controller is effective for systems with arbitrarily large subsystem interconnections. The output-feedback decentralized controller is effective for single-input single-output subsystems that are minimum phase and relative degree one. The decentralized controller achieves asymptotic stabilization and disturbance rejection in the presence of an unknown disturbance, which is generated by an unknown Lyapunov-stable linear system.

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The performance modelling of large-scale systems using discrete-state approaches is fundamentally hampered by the well-known problem of state-space explosion, which causes exponential growth of the reachable state space as a function of the number of the components which constitute the model. Because they are mapped onto continuous-time Markov chains (CTMCs), models described in the stochastic process algebra PEPA are no exception. This thesis presents a deterministic continuous-state semantics of PEPA which employs ordinary differential equations (ODEs) as the underlying mathematics for the performance evaluation. This is suitable for models consisting of large numbers of replicated components, as the ODE problem size is insensitive to the actual population levels of the system under study. Furthermore, the ODE is given an interpretation as the fluid limit of a properly defined CTMC model when the initial population levels go to infinity. This framework allows the use of existing results which give error bounds to assess the quality of the differential approximation. The computation of performance indices such as throughput, utilisation, and average response time are interpreted deterministically as functions of the ODE solution and are related to corresponding reward structures in the Markovian setting. The differential interpretation of PEPA provides a framework that is conceptually analogous to established approximation methods in queueing networks based on meanvalue analysis, as both approaches aim at reducing the computational cost of the analysis by providing estimates for the expected values of the performance metrics of interest. The relationship between these two techniques is examined in more detail in a comparison between PEPA and the Layered Queueing Network (LQN) model. General patterns of translation of LQN elements into corresponding PEPA components are applied to a substantial case study of a distributed computer system. This model is analysed using stochastic simulation to gauge the soundness of the translation. Furthermore, it is subjected to a series of numerical tests to compare execution runtimes and accuracy of the PEPA differential analysis against the LQN mean-value approximation method. Finally, this thesis discusses the major elements concerning the development of a software toolkit, the PEPA Eclipse Plug-in, which offers a comprehensive modelling environment for PEPA, including modules for static analysis, explicit state-space exploration, numerical solution of the steady-state equilibrium of the Markov chain, stochastic simulation, the differential analysis approach herein presented, and a graphical framework for model editing and visualisation of performance evaluation results.

Cette thèse considère les réseaux de capteurs sans fil (RCSF) de grande dimension (de l'ordre du million de noeuds). Les questions posées sont les suivantes : comment prédire le bon fonctionnement et calculer avant déploiement les performances d'un tel réseau, sachant qu'aucun simulateur ne peut simuler un réseau de plus de 100 000 noeuds ? Comment assurer sa configuration pour garantir performance, passage à l'échelle, robustesse et durabilité ? La solution proposée dans cette thèse s'appuie sur une architecture de RCSF hétérogène à deux niveaux, dont le niveau inférieur est composé de capteurs et le niveau supérieur de collecteurs. La première contribution est un algorithme d'auto-organisation multi-canal qui permet de partitionner le réseau inférieur en plusieurs sous-réseaux disjoints avec un collecteur et un canal de fréquence par sous-réseau tout en respectant le principe de réutilisation de fréquence. La seconde contribution est l'optimisation du déploiement des collecteurs car leur nombre représente celui des sous-réseaux. Les problèmes traités ont été : l'optimisation des emplacements des puits pour un nombre prédéfini de puits et la minimisation du nombre de puits ou du coût pour un nombre prédéfini de sauts dans les sous-réseaux. Une solution intuitive et appropriée pour assurer à la fois performances réseaux et coût, est de partitionner le réseau inférieur en sous-réseaux équilibrés en nombre de sauts. Pour ce faire, la topologie physique des puits est une répartition géographique régulière en grille (carrée, triangulaire, etc.). Des études théoriques et expérimentales par simulation des modèles de topologie montrent, en fonction des besoins applicatifs et physiques, la méthodologie de choix et le calcul des meilleures solutions de déploiement. / This thesis considers the large-scale wireless sensor network (LSWSN) consisting of million nodes. The questions are: how to predict the good working and to compute before deployment the performances of such a network, knowing that no simulator can simulate a network of more than 100000 nodes? How to ensure its configuration to ensure performance, scalability, robustness and longevity? The solution proposed in this thesis is based on a two-tiered heterogeneous architecture of WSN in which the level 1 is composed of sensors and the level 2 of collectors. The first contribution is a multi-channel self-organization algorithm, which allows partitioning the network of level 1 into several disjointed sub-networks with one collector and one frequency channel while respecting the principle of frequency reuse. The second contribution is to optimize the deployment of collectors because their number represents that of sub-networks. The problems addressed were: the optimization of sinks locations for a predetermined number of sinks, and the minimization of financial cost related of the sinks? number, for a predetermined number of hops in the sub-networks. An intuitive and appropriate solution to ensure both network performance and cost is to partition the network of level 1 into balanced sub-networks in number of hops. To do this, the physical topology of sinks is a regular geographical grid (square, triangular, etc.). Theoretical studies and simulation of topology models show, depending on application requirements (node density, charge application, etc.) and physical (radio range, surveillance zone), the methodology of choice and the computation of the best deployment solutions.