We investigate the stabilization of unstable multidimensional partially observed single-station, multi-sensor (single-controller) and multi-controller (single-sensor) linear systems controlled over discrete noiseless channels under fixed-rate information constraints. Stability is achieved under communication requirements that are asymptotically tight in the limit of large sampling periods. Through the use of similarity transforms, sampling and random-time drift conditions we obtain a coding and control policy leading to the existence of a unique invariant distribution and finite second moment for the sampled state. We use a vector stabilization scheme in which all modes of the linear system visit a compact set together infinitely often. / Thesis (Master, Mathematics & Statistics) -- Queen's University, 2012-12-06 15:06:37.449

There is an increasing need for low-power neural systems as neural networks become more widely used in embedded devices with limited resources. Spiking neural networks (SNNs) are proving to be a more energy-efficient option to conventional Artificial neural networks (ANNs), which are recognized for being computationally heavy. Despite its significance, there has been not enough attention on training SNNs on large-scale distributed Machine Learning techniques like Federated Learning (FL). As federated learning involves many energy-constrained devices, there is a significant opportunity to take advantage of the energy efficiency offered by SNNs. However, it is necessary to address the real-world communication constraints in an FL system and this is addressed with the help of three communication reduction techniques, namely, model compression, partial device participation, and periodic aggregation. Furthermore, the convergence of federated learning systems is also affected by data heterogeneity. Federated learning systems are capable of protecting the private data of clients from adversaries. However, by analyzing the uploaded client parameters, confidential information can still be revealed. To combat privacy attacks on the FL systems, various attempts have been made to incorporate differential privacy within the framework. In this thesis, we investigate the trade-offs between communication costs and training variance under a Federated Learning system with Differential Privacy applied at the parameter server (curator model). / Master of Science / Federated Learning is a decentralized method of training neural network models; it employs several participating devices to independently learn a model on their local data partition. These local models are then aggregated at a central server to achieve the same performance as if the model had been trained centrally. But with Federated Learning systems there is a communication overhead accumulated. Various communication reductions can be used to reduce these costs. Spiking Neural Networks, being the energy-efficient option to Artificial Neural Networks, can be utilized in Federated Learning systems. This is because FL systems consist of a network of energy-efficient devices. Federated learning systems are helpful in preserving the privacy of data in the system. However, an attacker can still obtain meaningful information from the parameters that are transmitted during a session. To this end, differential privacy techniques are utilized to combat privacy concerns in Federated Learning systems. In this thesis, we compare and contrast different communication costs and parameters of a federated learning system with differential privacy applied to it.

Analysis, design and implementation of cooperative control strategies for multi-robot systems under communication constraints is the topic of this thesis. Motivated by a rapidly growing number of applications with networked robots and other vehicles, fundamental limits on the achievable collaborative behavior are studied for large teams of autonomous agents. In particular, a problem is researched in detail in which the group of agents is supposed to agree on a common state without any centralized coordination. Due to the dynamics of the individual agents and their varying connectivity, this problemis an extension of the classical consensus problemin computer science. It captures a crucial component of many desirable features of multi-robot systems, such as formation, flocking, rendezvous, synchronizing and covering. Analytical bounds on the convergence rate to consensus are derived for several systemconfigurations. It is shown that static communication networks that exhibit particular symmetries yield slow convergence, if the connectivity of each agent does not scale with the total number of agents. On the other hand, some randomly varying networks allow fast convergence even if the connectivity is low. It is furthermore argued that if the data being exchanged between the agents are quantized, it may heavily degrade the performance. The extent to which certain quantization schemes are more suitable than others is quantified through relations between the number of agents and the required total network bit rate. The design of distributed coordination and estimation schemes based on the consensus algorithm is presented. A receding horizon coordination strategy utilizing subgradient optimization is developed. Robustness and implementation aspects are discussed. A new collaborative estimation method is also proposed. The implementation of multi-robot control systems is difficult due to the high systemcomplexity. In the final part of this thesis, a hierarchical control architecture appropriate for a class of coordination tasks is therefore suggested. It allows a formal verification of the correctness of the implemented control algorithms. / QC 20100920

This thesis describes a change to the Physiocomimetics Robotic Swarm Control framework that implements communication constraints into swarm behavior. These constraints are necessary to successfully implement theoretical applications in the real world. We describe the basic background of swarm robotics, the Physiocomimetics framework and methods that have attempted to implement communications constraints into robotic swarms. The Framework is changed by the inclusion of different virtual particles at a global and local scale that only cause a force on swarm elements if those elements are disconnected from a swarm network. The global particles introduced are a point of known connectivity and a global centroid of the swarm. The local particles introduced are the point of last connectivity and a local centroid. These particles are tested in various simulations and the results are discussed. The global particles are very effective at insuring the communication constraints of the swarm, but the local particles only have partial success. Additionally, some observations are made about swarm formations and the effect of the communication range used during swarm formation.

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Les travaux développés dans cette thèse portent sur la "co-conception diagnostic / ordonnancement des mesures dans un système contrôlé en réseau" qui est un sujet multidisciplinaire nécessitant des compétences en théorie du contrôle et en théorie des réseaux. La thèse a pour but de développer, dans le contexte des systèmes contrôlés en réseau, une approche de co-conception qui intègre de façon coordonnée les caractéristiques qui expriment la performance du diagnostic des défauts et les paramètres de l'ordonnancement temps-réel des messages. L'intérêt de cette approche coordonnée réside essentiellement dans la minimisation des ressources nécessaires pour atteindre la performance du diagnostic requise, minimisation qui prend tout son sens dans le contexte des systèmes embarqués. Nous nous sommes intéressés plus particulièrement à l'étude des problèmes liés à l'élaboration d'algorithmes de diagnostic efficaces et adaptés aux caractéristiques de l'application de façon tout en prenant en compte différents types de contraintes liées au réseau. En conjonction avec ces algorithmes, deux ensembles de techniques d'ordonnancement des mesures ont été développés : - ordonnancement hors ligne - ordonnancement évènementiel en ligne Pour l'ordonnancement hors ligne, les séquences de communication sont conçues en amont, préalablement à la mise en oeuvre de l'algorithme de diagnostic (implémentation). D'autre part, nous proposons aussi des techniques d'ordonnancement en ligne basées sur l'échantillonnage évènementiel développé au cours de la dernière décennie. Au contraire de la plupart des recherches en automatique classique, considérant que l'échantillonnage des signaux continus est réalisé d'une manière périodique, les mesures dans cette approche sont transmises si et seulement si la condition de transmission (évènement) est vérifiée / The works developed in this thesis deal with 'fault diagnosis and sensor scheduling co-design' in networked control system. This multidisciplinary subject requires theoretical knowledge in both fault diagnosis and communication networks. Our contribution consists in developing a co-design approach that integrates in the same framework the characteristics of fault diagnosis performance and real time sensor scheduling. The main benefit of this approach is minimizing the required network resources for attending acceptable fault diagnosis performances. We are interested in the development of more efficient and more adapted for real time implementation fault diagnosis algorithms while taking into account different types of communication constraints. In conjunction with these algorithms, two sets of sensor scheduling techniques are used : - Off-line scheduling - On-line scheduling (event triggered sampling) For off-line scheduling, the communication sequences are designed before the implementation of the diagnostic algorithm. In this context, we proposed several techniques for scheduling with different spatial and temporal complexity and adapted to different operating condition for the detection and the isolation of faults based on the information provided by the selected communication sequences. Moreover, we deal also with on-line scheduling techniques based on the event triggered sampling developed during the last decade. In This approach measurement packets are transmitted if and only if the transmission condition (event) is verified. This saves resources provided by the network while maintaining acceptable performance of fault diagnosis. The objective of these algorithms is to minimize the number of transmitted information which means less energy consumption and has a major importance in wireless networked control systems

Les Systèmes de Contrôle Embarqués Distribués (SCED) utilisent les réseaux de communication dans les boucles de rétroaction. Étant donné que les systèmes SCED ont une puissance de batterie, une bande passante de communication et une puissance de calcul limitée, les débits des données ou des informations transmises sont bornées et ils peuvent affecter leur stabilité. Ceci nous amène à élargir le spectre de notre étude et y intégrer une étude sur la relation entre la théorie du contrôle d’un coté et celle de l’information de l’autre. La contrainte de débit de données induit la quantification des signaux tandis que les aspects de calcul temps réel et de communication induit des événements asynchrones qui ne sont plus réguliers ou périodiques. Ces deux phénomènes donnent au SCED une double nature, continue et discrète, et en font des cas d’étude spécifiques. Dans cette thèse, nous analysons la stabilité et la performance de SCED du point de vue de la théorie de l’information et du contrôle. Pour les systèmes linéaires, nous montrons l’importance du compromis entre la quantité d’information communiquée et les objectifs de contrôle, telles que la stabilité, la contrôlabilité/observabilité et les performances. Une approche de conception conjointe de contrôle et de communication (en termes de débit d’information au sens de Shannon) des SCED est étudiée. Les principaux résultats de ces travaux sont les suivants : nous avons prouvé que la réduction d’entropie (ce qui correspond à la réduction d’incertitude) dépend du Grammien de contrôlabilité. Cette réduction est également liée à l’information mutuelle de Shannon. Nous avons démontré que le Grammien de contrôlabilité constitue une métrique de l’entropie théorique de l’information en ce qui concerne les bruits induits par la quantification. La réduction de l’influence de ces bruits est équivalente à la réduction de la norme du Grammien de contrôlabilité. Nous avons établi une nouvelle relation entre la matrice d’information de Fisher (FIM) et le Grammien de Contrôlabilité (CG) basé sur la théorie de l’estimation et la théorie de l’information. Nous proposons un algorithme qui distribue de manière optimale les capacités de communication du réseau entre un nombre "n" d’actionneurs et/ou systèmes concurrents se basant sur la réduction de la norme du Grammien de Contrôlabilité / The Networked Embedded Control System (NECS) uses communication networks in the feedback loops. Since the embedded systems have the limited battery power along with limited bandwidth and computing power, the feedback data rates are limited. The rate of communications can drastically affect system stability. Hence, there is a strong need for understanding and merging the Control Theory with Communication or Information Theory. The data rate constraint introduces quantization into the feedback loop whereas the communication or computational model induces discrete events which are no more periodic. These two phenomena give the NECS a twofold nature : continuous and discrete, and render them specific. In this thesis we analyze the stability and performance of NECS from Informationtheoretic point of view. For linear systems, we show how fundamental are the tradeoffs between the communication-rate and control goals, such as stability, controllability / observability and performances. An integrated approach of control and communication (in terms of Shannon Information Rate) of NECS or distributed embedded control systems is studied. The main results are as follows : We showed that the entropy reduction which is same as uncertainty reduction is dependent on Controllability Gramian only. It is also related to Shannon Mutual-Information. We demonstrated that the gramian of controllability constitutes a metric of information theoretic entropy with respect to the noises induced by quantization. Reduction of these noises is equivalent to the design methods proposing a reduction of the controllability gramian norm. We established a new relation of Fisher Information Matrix (FIM) and Controllability Gramian (CG) based on estimation-theoretic and information-theoretic explanations. We propose an algorithm which optimally distributes the network capacity between a number "n" of competing actuators. The metric of this distribution is the Controllability Gramian