La commande événementielle offre une alternative prometteuse à la commande périodique classique, qui est considérée comme peu économe vis-à-vis des ressources. Contrairement à la commande classique, la commande événementielle propose de passer d'une loi de commande en temps continu à une loi de commande numérique à travers un échantillonnage non-uniforme. Dans ce cas, une nouvelle valeur de la loi de commande n'est calculée que lorsque la réponse du système est inadmissible. En revanche, la valeur de la loi de commande est maintenue constante si la réponse est satisfaisante. Dans cette thèse, nous explorons des moyens de réduire le nombre de mise à jour de la loi de commande, et de rallonger les intervalles de temps entre les mises à jour.Dans le cas des systèmes linéaires, nous présentons un algorithme de stabilisation dans lequel nous relaxons les conditions de stabilité sur la fonction de Lyapunov du système. Pour induire moins d'échantillons, on requiert uniquement que cette fonction soit maintenue sous un seuil décroissant. Le calcul des paramètres optimaux du seuil est transformé en problème de valeurs propres généralisées. Ensuite, cette approche est étendue aux systèmes dits switched, et une version self-triggered est proposée. Nous traitons également le problème de suivi de trajectoire par une commande événementielle. Enfin, dans le cas du contrôle des systèmes non-linéaires, nous proposons d'utiliser une analyse de contraction des trajectoires, et ce à cause de la difficulté de trouver une fonction de Lyapunov pour ces systèmes. / Event-triggered control offers a promising alternative to the classical, resource-consuming, periodic control. It suggests to replace the periodic, high frequency sampling used in the continuous-to-discrete transformations of control signals with aperiodic sampling. A new value of the event-triggered control law is computed only when the system's response is unsatisfactory. The control value is kept constant otherwise. In this thesis, we explore ways to induce fewer updates, and to have longer intervals between two samples. We also seek to make the algorithms that we design more detailed, by describing how to choose or compute the optimal parameters.In the linear case, we present a stabilizing algorithm in which we relax the stability conditions on the system's Lyapunov function to produce fewer, sparser updates of the control law. Stability is ensured by maintaining the Lyapunov function below a certain decreasing threshold. The optimal threshold function is derived by solving a maximum generalized eigenvalue problem. This approach is then extended to switched linear systems. We also present a self-triggered version of this algorithm using Newton methods for optimization and root-finding. The reference tracking problem is treated in the event-triggered control framework as well. Finally, in the nonlinear case, due to the difficulty of finding a Lyapunov function, we explore the use of contraction analysis. This approach allows us to describe the nonlinear event-triggered control algorithm more thoroughly than if we had used Lyapunov techniques.

The feedback control system is defined as the sampling of an output signal and feeding it back to the input, resulting in an error signal that drives the overall system. This dissertation focuses on the stabilization and performance of state feedback control systems. Chapters 3 and 4 focus on the feedback control protocol approaching in the multi-agents system. In particular, the global regulation of distributed optimization problems has been considered. Firstly, we propose a distributed optimization algorithm based on the proportional-integral control strategy and the exponential convergence rate has been delivered. Moreover, a decentralized mechanism has been equipped to the proposed optimization algorithm, which enables an arbitrarily chosen agent in the system can compute the value of the optimal solution by only using the successive local states. After this, we consider the cost function follows the restricted secant inequality. A dynamic event-triggered mechanism design has been proposed. By ensuring the global regulation of the distributed proportional-integral optimization algorithm, the dynamic event-triggered mechanism efficiently reduces the communication frequency among agents. Chapter 5 focuses on the feedback control protocol approaching the single-agent system. Specifically, we investigate the truncated predictor feedback control of the regulation of linear input-delayed systems. For the purpose of improving the closed-loop performance, we propose a design of the truncated predictor feedback method with time-varying feedback parameters and give the potential range of choosing the time-varying feedback parameters to replace the traditional constant low gain parameters.

Networked control systems, commonly employed in domains such as space exploration and robotics utilize network communication for efficient and coordinated control among distributed components. In these scenarios, effectively managing communication to prevent network overload poses a critical challenge. Previous research has explored the use of reinforcement learning methods combined with event-triggered control to autonomously have agents learn efficient policies for control and communication. Nevertheless, these approaches have encountered limitations in terms of performance and scalability when applied in multiagent scenarios. This thesis examines the underlying causes of these challenges and propose potential solutions. With the findings suggesting that training agents in a decentralized manner, coupled with modeling of the missing communication, can improve agent performance. This allows the agents to achieve performance levels comparable to those of agents trained with full communication, while reducing unnecessary communication

Networked control over wireless sensor and actuator systems is of growing importancein many application domains. Energy and communication bandwidth are scarce resources in such systems. Despite that feedback control might only be needed occasionally, sensor and actuator communications are often periodic and with high frequency in today’s implementations. In this thesis, resource-constrained wireless networked control systems with an adaptive sampling period are considered. Our first contribution is a system architecture for aperiodic wireless networked control. As the underlying data transmission is performed over a shared wireless network, we identify scheduling policies and medium access controls that allow for an efficient implementation of sensor communication. We experimentally validate three proposed mechanisms and show that best performance is obtained by a hybrid scheme, combining the advantages of event- and self-triggered control as well as the possibilities provided by contention-based and contention-free medium accesscontrol. In the second contribution, we propose an event-triggered PI controller for wireless process control systems. A novel triggering mechanism which decides the transmission instants based on an estimate of the control signal is proposed. It addresses some side-effects that have been discovered in previous PI proposals, which trigger on the state of the process. Through simulations we demonstrate that the new PI controller provides setpoint tracking and disturbance rejection close to a periodic PI controller, while reducing the required network resources. The third contribution proposes a co-design of feedback controllers and wireless medium access. The co-design is formulated as a constrained optimization problem, whereby the objective function is the energy consumption of the network and the constraints are the packet loss probability and delay, which are derived from the performance requirements of the control systems. The design framework is illustrated in a numerical example. / QC 20111004

Les systèmes multi-agents (MAS) et la commande coopérative ont fait l'objet de nombreuses recherches ces dernières années. Les domaines d'application sont très diverses et dans le cas des systèmes multi-véhicules, des approches ont été développées pour des unmanned air vehicles (UAVs), satellites, avions... Les types de missions envisagées sont des missions complexes telles l’exploration ou la surveillance de zones, la recherche et le suivi de cibles d'intérêt. Cependant, la coopération requière des échanges de communication entre les agents. Lorsque ceux-ci sont nombreux, cet échange peut conduire à des saturations du réseau, à l'augmentation des délais de transmission ou l’occurrence de pertes de paquets, d'où l'intérêt de réduire le nombre de communication. Dans les méthodes event-triggered, une communication est envoyée quand une condition, basée sur des paramètres choisis et un seuil prédéfini, est remplie. La principale difficulté est de définir une condition qui permettra de limiter les échanges sans dégrader l'exécution de la mission choisie. Dans le cas d'un système distribué, chaque agent doit maintenir une estimation de la valeur de l'état des autres agents afin de remplacer l'absence d'informations due à la communication réduite. L'objectif de cette thèse est de développer des lois de commandes et des estimateurs distribuées pour un système multi-agent afin de réduire le nombre de communication par méthode event-triggered, tout en prenant en compte la présence de perturbations. L'étude est divisée en deux grandes parties. La première décrit une méthode de communication event-triggered permettant de converger vers un consensus pour un système multi-agents de modèle d'évolution dynamique linéaire généralisée et en présence de perturbations d'état. Pour réduire les communications, un estimateur précis de l'état des agents est proposé, couplé à un estimateur de l'estimation de l'erreur, ainsi qu'un protocole de communication adapté. En prenant en compte la commande appliquée à chaque agent, l'estimateur proposé permet d'obtenir un consensus avec un nombre bien inférieur de communication que de la méthode de référence dans l'état de l'art. La seconde partie propose une stratégie de réduction de communication pour une commande de vol en formation permettant de suivre une trajectoire de référence. La dynamique des agents est décrite par un système Euler-Lagrange incluant des perturbations et des méconnaissances sur les paramètres du modèle. Différentes structures d'estimateurs sont proposées pour reconstruire les informations manquantes. La condition d'event-triggered distribuée proposée est basée sur l'écart relatif entre les positions et vitesses réelles et désirées des agents, ainsi que l'erreur relative entre la valeur estimée de l'état de l'agent et la valeur réelle. Une trajectoire de référence unique est déterminée pour guider la flotte. L'effet des perturbations sur la formation et la communication a été analysé. Enfin, les méthodes proposées ont été adaptées pour tenir compte des dégradations de performances dues aux pertes de données et aux délais de communication. Pour les deux types d'approches présentées les conditions de la stabilité du MAS ont été obtenues par l'intermédiaire de fonctions de Lyapunov et l'absence de paradoxe de Zeno a été étudiée. / A large amount of research work has been recently dedicated to the study of Multi-Agent System and cooperative control. Applications to mobile robots, like unmanned air vehicles (UAVs), satellites, or aircraft have been tackled to insure complex mission such as exploration or surveillance. However, cooperative tasking requires communication between agents, and for a large number of agents, the number of communication exchanges may lead to network saturation, increased delays or loss of transferred packets, from the interest in reducing them. In event-triggered strategy, a communication is broadcast when a condition, based on chosen parameters and some threshold, is fulfilled. The main difficulty consists in determining the communication triggering condition (CTC) that will ensure the completion of the task assigned to the MAS. In a distributed strategy, each agent maintains an estimate value of others agents state to replace missing information due to limited communication. This thesis focuses on the development of distributed control laws and estimators for multi-agent system to limit the number of communication by using event-triggered strategy in the presence of perturbation with two main topics, i.e. consensus and formation control. The first part addresses the problem of distributed event-triggered communications for consensus of a multi-agent system with both general linear dynamics and state perturbations. To decrease the amount of required communications, an accurate estimator of the agent states is introduced, coupled with an estimator of the estimation error, and adaptation of communication protocol. By taking into account the control input of the agents, the proposed estimator allows to obtain a consensus with fewer communications than those obtained by a reference method. The second part proposes a strategy to reduce the number of communications for displacement-based formation control while following a desired reference trajectory. Agent dynamics are described by Euler-Lagrange models with perturbations and uncertainties on the model parameters. Several estimator structures are proposed to rebuild missing information. The proposed distributed communication triggering condition accounts for inter-agent displacements and the relative discrepancy between actual and estimated agent states. A single a priori trajectory has to be evaluated to follow the desired path. Effect of state perturbations on the formation and on the communications is analyzed. Finally, the proposed methods have been adapted to consider packet dropouts and communication delays. For both types of problems, Lyapunov stability of the MAS has been developed and absence of Zeno behavior is studied.

In this dissertation, we study multi-robot coordination in the context of multi-target tracking. Specifically, we are interested in the coordination achieved by means of submodular function optimization. Submodularity encodes the diminishing returns property that arises in multi-robot coordination. For example, the marginal gain of assigning an additional robot to track the same target diminishes as the number of robots assigned increases. The advantage of formulating coordination problems as submodular optimization is that a simple, greedy algorithm is guaranteed to give a good performance. However, often this comes at the expense of unrealistic models and assumptions. For example, the standard formulation does not take into account the fact that robots may fail, either randomly or due to adversarial attacks. When operating in uncertain conditions, we typically seek to optimize the expected performance. However, this does not give any flexibility for a user to seek conservative or aggressive behaviors from the team of robots. Furthermore, most coordination algorithms force robots to communicate at each time step, even though they may not need to. Our goal in this dissertation is to overcome these limitations by devising coordination algorithms that are parsimonious in communication, allow a user to manage the risk of the robot performance, and are resilient to worst-case robot failures and attacks. In the first part of this dissertation, we focus on designing parsimonious communication strategies for target tracking. Specifically, we investigate the problem of determining when to communicate and who to communicate with. When the robots use range sensors, the tracking performance is a function of the relative positions of the robots and the targets. We propose a self-triggered communication strategy in which a robot communicates its own position with its neighbors only when a certain set of conditions are violated. We prove that this strategy converges to the optimal robot positions for tracking a single target and in practice, reduces the number of communication messages by 30%. When tracking multiple targets, we can reduce the communication by forming subsets of robots and assigning one subset to track a target. We investigate a number of measures for tracking quality based on the observability matrix and show which ones are submodular and which ones are not. For non-submodular measures, we show a greedy algorithm gives a 1/(n+1) approximation, if we restrict the subset to n robots. In optimizing submodular functions, a common assumption is that the function value is deterministic, which may not hold in practice. For example, the sensor performance may depend on environmental conditions which are not known exactly. In the second part of the dissertation, we design an algorithm for stochastic submodular optimization. The standard formulation for stochastic optimization optimizes the expected performance. However, the expectation is a risk-neutral measure. Instead, we optimize the Conditional Value-at-Risk (CVaR), which allows the user the flexibility of choosing a risk level. We present an algorithm, based on the greedy algorithm, and prove that its performance has bounded suboptimality and improves with running time. We also present an online version of the algorithm to adapt to real-time scenarios. In the third part of this dissertation, we focus on scenarios where a set of robots may fail naturally or due to adversarial attacks. Our objective is to track as many targets as possible, a submodular measure, assuming worst-case robot failures. We present both centralized and distributed resilient tracking algorithms to cope with centralized and distributed communication settings. We prove these algorithms give a constant-factor approximation of the optimal in polynomial running time. / Doctor of Philosophy / Today, robotics and autonomous systems have been increasingly used in various areas such as manufacturing, military, agriculture, medical sciences, and environmental monitoring. However, most of these systems are fragile and vulnerable to adversarial attacks and uncertain environmental conditions. In most cases, even if a part of the system fails, the entire system performance can be significantly undermined. As robots start to coexist with humans, we need algorithms that can be trusted under real-world, not just ideal conditions. Thus, this dissertation focuses on enabling security, trustworthiness, and long-term autonomy in robotics and autonomous systems. In particular, we devise coordination algorithms that are resilient to attacks, trustworthy in the face of the uncertain conditions, and allow the long-term operation of multi-robot systems. We evaluate our algorithms through extensive simulations and proof-of-concept experiments. Generally speaking, multi-robot systems form the "physical" layer of Cyber-Physical Sytems (CPS), the Internet of Things (IoT), and Smart City. Thus, our research can find applications in the areas of connected and autonomous vehicles, intelligent transportation, communications and sensor networks, and environmental monitoring in smart cities.

Cyber-physical systems (CPSs) tightly integrate physical processes with computing and communication to autonomously interact with the surrounding environment.This enables emerging applications such as autonomous driving, coordinated flightof swarms of drones, or smart factories. However, current technology does notprovide the reliability and flexibility to realize those applications. Challenges arisefrom wireless communication between the agents and from the complexity of thesystem dynamics. In this thesis, we take on these challenges and present three maincontributions.We first consider imperfections inherent in wireless networks, such as communication delays and message losses, through a tight co-design. We tame the imperfectionsto the extent possible and address the remaining uncertainties with a suitable controldesign. That way, we can guarantee stability of the overall system and demonstratefeedback control over a wireless multi-hop network at update rates of 20-50 ms.If multiple agents use the same wireless network in a wireless CPS, limitedbandwidth is a particular challenge. In our second contribution, we present aframework that allows agents to predict their future communication needs. Thisallows the network to schedule resources to agents that are in need of communication.In this way, the limited resource communication can be used in an efficient manner.As a third contribution, to increase the flexibility of designs, we introduce machinelearning techniques. We present two different approaches. In the first approach,we enable systems to automatically learn their system dynamics in case the truedynamics diverge from the available model. Thus, we get rid of the assumption ofhaving an accurate system model available for all agents. In the second approach, wepropose a framework to directly learn actuation strategies that respect bandwidthconstraints. Such approaches are completely independent of a system model andstraightforwardly extend to nonlinear settings. Therefore, they are also suitable forapplications with complex system dynamics. / <p>QC 20190118</p>

In modern cyber-physical systems (CPSs) where the control signals are generally transmitted via shared communication networks, there is a desire to balance the closed-loop control performance with the communication cost necessary to achieve it. In this context, aperiodic real-time scheduling of control tasks comes into being and has received increasing attention recently. It is well known that model predictive control (MPC) is currently widely utilized in industrial control systems and has greatly increased profits in comparison with the proportional integral-derivative (PID) control. As communication and networks play more and more important roles in modern society, there is a great trend to upgrade and transform traditional industrial systems into CPSs, which naturally requires extending conventional MPC to communication-efficient MPC to save network resources. Motivated by this fact, we in this thesis propose robust MPC and scheduling co-design algorithms to networked CPSs possibly affected by both parameter uncertainties and additive disturbances. In Chapter 2, a dynamic event-triggered robust tube-based MPC for constrained linear systems with additive disturbances is developed, where a time-varying pre-stabilizing gain is obtained by interpolating multiple static state feedbacks and the interpolating coefficient is determined via optimization at the time instants when the MPC-based control is triggered. The original constraints are properly tightened to achieve robust constraint optimization and a sequence of dynamic sets used to test events are derived according to the optimized coefficient. We theoretically show that the proposed algorithm is recursively feasible and the closed-loop system is input-to-state stable (ISS) in the attraction region. Numerical results are presented to verify the design. In Chapter 3, a self-triggered min-max MPC strategy is developed for constrained nonlinear systems subject to both parametric uncertainties and additive disturbances, where the robust constraint satisfaction is achieved by considering the worst case of all possible uncertainty realizations. First, we propose a new cost function that relaxes the penalty on the system state in a time period where the controller will not be invoked. With this cost function, the next triggering time instant can be obtained at current time instant by solving a min-max optimization problem where the maximum triggering period becomes a decision variable. The proposed strategy is proved to be input-to-state practical stable (ISpS) in the attraction region at triggering time instants under some standard assumptions. Extensions are made to linear systems with additive disturbances, for which the conditions reduce to a linear matrix inequality (LMI). Comprehensive numerical experiments are performed to verify the correctness of the theoretical results. / Graduate

<p>Day by day, we are witnessing a considerable increase in number and range of applications which entail the use of embedded computer systems. This increase is closely followed by the growth in complexity of applications controlled by embedded systems, often involving strict timing requirements, like in the case of safety-critical applications. Efficient design of such complex systems requires powerful and accurate tools that support the designer from the early phases of the design process.</p><p>This thesis focuses on the study of real-time distributed embedded systems and, in particular, we concentrate on a certain aspect of their real-time behavior and implementation: the time-triggered (TT) and event-triggered (ET) nature of the applications and of the communication protocols. Over the years, TT and ET systems have been usually considered independently, assuming that an application was entirely ET or TT. However, nowadays, the growing complexity of current applications has generated the need for intermixing TT and ET functionality. Such a development has led us to the identification of several interesting problems that are approached in this thesis. First, we focus on the elaboration of a holistic schedulability analysis for heterogeneous TT/ET task sets which interact according to a communication protocol based on both static and dynamic messages. Second, we use the holistic schedulability analysis in order to guide decisions during the design process. We propose a design optimisation heuristic that partitions the task-set and the messages into the TT and ET domains, maps and schedules the partitioned functionality, and optimises the communication protocol parameters. Experiments have been carried out in order to measure the efficiency of the proposed techniques.</p> / Report code: LiU-Tek-Lic-2003:21.

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