Distributed Event-Triggered Control for Asymptotic Synchronization of Dynamical Networks

This paper studies synchronization of dynamical networks with event-based communication. Firstly, two estimators are introduced into each node, one to estimate its own state, and the other to estimate the average state of its neighbours. Then, with these two estimators, a distributed event-triggering rule (ETR) with a dwell time is designed such that the network achieves synchronization asymptotically with no Zeno behaviours. The designed ETR only depends on the information that each node can obtain, and thus can be implemented in a decentralized way.Comment: 8 pages, 2 figues, 1 tabl

Non-linear ICA Analysis of Resting-State fMRI in Mild Cognitive Impairment

Compared to linear independent component analysis (ICA), non-linear ICA is more suitable for the decomposition of mixed components. Existing studies of functional magnetic resonance imaging (fMRI) data by using linear ICA assume that the brain's mixed signals, which are caused by the activity of brain, are formed through the linear combination of source signals. But the application of the non-linear combination of source signals is more suitable for the mixed signals of brain. For this reason, we investigated statistical differences in resting state networks (RSNs) on 32 healthy controls (HC) and 38 mild cognitive impairment (MCI) patients using post-nonlinear ICA. Post-nonlinear ICA is one of the non-linear ICA methods. Firstly, the fMRI data of all subjects was preprocessed. The second step was to extract independent components (ICs) of fMRI data of all subjects. In the third step, we calculated the correlation coefficient between ICs and RSN templates, and selected ICs of the largest spatial correlation coefficient. The ICs represent the corresponding RSNs. After finding out the eight RSNs of MCI group and HC group, one sample t-tests were performed. Finally, in order to compare the differences of RSNs between MCI and HC groups, the two-sample t-tests were carried out. We found that the functional connectivity (FC) of RSNs in MCI patients was abnormal. Compared with HC, MCI patients showed the increased and decreased FC in default mode network (DMN), central executive network (CEN), dorsal attention network (DAN), somato-motor network (SMN), visual network(VN), MCI patients displayed the specifically decreased FC in auditory network (AN), self-referential network (SRN). The FC of core network (CN) did not reveal significant group difference. The results indicate that the abnormal FC in RSNs is selective in MCI patients

Game-theoretic decentralized model predictive control of thermal appliances in discrete-event systems framework

This paper presents a decentralized model predictive control (MPC) scheme for thermal appliances coordination control in smart buildings. The general system structure consists of a set of local MPC controllers and a game-theoretic supervisory control constructed in the framework of discrete-event systems (DES). In this hierarchical control scheme, a set of local controllers work independently to maintain the thermal comfort level in different zones, and a centralized supervisory control is used to coordinate the local controllers according to the power capacity and the current performance. Global optimality is ensured by satisfying the Nash equilibrium at the coordination layer. The validity of the proposed method is assessed by a simulation experiment including two case studies. The results show that the developed control scheme can achieve a significant reduction of the peak power consumption while providing an adequate temperature regulation performance if the system is P-observable

An input-based triggering approach to leader-following problems

In this paper, an event-based leader-following strategy for synchronization of multi-agent systems (MASs) is considered. A model-based approach is adopted to predict the relative inter-node states between intermittent communications. The predicted values of the relative inter-node states are forwarded to the controller to calculate a piecewise continuous control signal. The communication between two linked agents is triggered according to a protocol based on their control inputs. The proposed leader-following strategy guarantees exponential state synchronization under time-dependent thresholds and bounded state synchronization under constant thresholds, respectively. Furthermore, the elapsed time between any two successive triggering instants for any pair of linked agents is lower bounded by a constant. The communication frequency reduction potential of the proposed leader-following strategy is well demonstrated via a numerical example

Model Predictive Control for Demand Response Management Systems in Smart Buildings

RÉSUMÉ Les bâtiments représentent une portion importante de la consommation énergétique globale. Par exemple, aux USA, le secteur des bâtiments est responsable de 40% de la consommation énergétique totale. Plus de 50% de la consommation d’électricité est liée directement aux systèmes de chauffage, de ventilation et de climatisation (CVC). Cette réalité a incité beaucoup de chercheurs à développer de nouvelles solutions pour la gestion de la consommation énergétique dans les bâtiments, qui impacte la demande de pointe et les coûts associés. La conception de systèmes de commande dans les bâtiments représente un défi important car beaucoup d’éléments, tels que les prévisions météorologiques, les niveaux d’occupation, les coûts énergétiques, etc., doivent être considérés lors du développement de nouveaux algorithmes. Un bâtiment est un système complexe constitué d’un ensemble de sous-systèmes ayant différents comportements dynamiques. Par conséquent, il peut ne pas être possible de traiter ce type de systèmes avec un seul modèle dynamique. Récemment, différentes méthodes ont été développées et mises en application pour la commande de systèmes de bâtiments dans le contexte des réseaux intelligents, parmi lesquelles la commande prédictive (Model Predictive Control - MPC) est l’une des techniques les plus fréquemment adoptées. La popularité du MPC est principalement due à sa capacité à gérer des contraintes multiples, des processus qui varient dans le temps, des retards et des incertitudes, ainsi que des perturbations. Ce projet de recherche doctorale vise à développer des solutions pour la gestion de consommation énergétique dans les bâtiments intelligents en utilisant le MPC. Les techniques développées pour la gestion énergétique des systèmes CVC permet de réduire la consommation énergétique tout en respectant le confort des occupants et les contraintes telles que la qualité de service et les contraintes opérationnelles. Dans le cadre des MPC, différentes contraintes de capacité énergétique peuvent être imposées pour répondre aux spécifications de conception pendant la durée de l’opération. Les systèmes CVC considérés reposent sur une architecture à structure en couches qui réduit la complexité du système, facilitant ainsi les modifications et l’adaptation. Cette structure en couches prend également en charge la coordination entre tous les composants. Étant donné que les appareils thermiques des bâtiments consomment la plus grande partie de la consommation électrique, soit plus du tiers sur la consommation totale d’énergie, la recherche met l’emphase sur la commande de ce type d’appareils. En outre, la propriété de dynamique lente, la flexibilité de fonctionnement et l’élasticité requise pour les performances des appareils thermiques en font de bons candidats pour la gestion réponse à la demande (Demand Response - DR) dans les bâtiments intelligents.----------ABSTRACT Buildings represent the biggest consumer of global energy consumption. For instance, in the US, the building sector is responsible for 40% of the total power usage. More than 50% of the consumption is directly related to heating, ventilation and air-conditioning (HVAC) systems. This reality has prompted many researchers to develop new solutions for the management of HVAC power consumption in buildings, which impacts peak load demand and the associated costs. Control design for buildings becomes increasingly challenging as many components, such as weather predictions, occupancy levels, energy costs, etc., have to be considered while develop-ing new algorithms. A building is a complex system that consists of a set of subsystems with di˙erent dynamic behaviors. Therefore, it may not be feasible to deal with such a system with a single dynamic model. In recent years, a rich set of conventional and modern control schemes have been developed and implemented for the control of building systems in the context of the Smart Grid, among which model redictive control (MPC) is one of the most-frequently adopted techniques. The popularity of MPC is mainly due to its ability to handle multiple constraints, time varying processes, delays, uncertainties, as well as disturbances. This PhD research project aims at developing solutions for demand response (DR) man-agement in smart buildings using the MPC. The proposed MPC control techniques are im-plemented for energy management of HVAC systems to reduce the power consumption and meet the occupant’s comfort while taking into account such restrictions as quality of service and operational constraints. In the framework of MPC, di˙erent power capacity constraints can be imposed to test the schemes’ robustness to meet the design specifications over the operation time. The considered HVAC systems are built on an architecture with a layered structure that reduces the system complexity, thereby facilitating modifications and adaptation. This layered structure also supports the coordination between all the components. As thermal appliances in buildings consume the largest portion of the power at more than one-third of the total energy usage, the emphasis of the research is put in the first stage on the control of this type of devices. In addition, the slow dynamic property, the flexibility in operation, and the elasticity in performance requirement of thermal appliances make them good candidates for DR management in smart buildings

Finite-time sliding mode control strategies and their applications

In many engineering applications, faster convergence is always sought, such as manufacturing plants, defence sectors, mechatronic systems. Nowadays, most of the physical systems are operated in a closed-loop environment in conjunction with a controller. Therefore, the controller plays a critical role in determining the speed of the convergence of the entire closed-loop system. Linear controllers are quite popular for their simple design. However, linear controllers provide asymptotic convergence speed, i.e., the actual convergence is obtained when the time reaches an infinitely large amount. Furthermore, linear controllers are not entirely robust in the presence of non-vanishing types of disturbances. It is always important to design robust controllers because of the presence of model imperfections and unknown disturbances in almost all kinds of systems. Therefore, it is necessary to design controllers that are not only robust, but will also provide faster convergence speed. Out of many robust non-linear control strategies, a further development in sliding mode control (SMC) strategy is considered in this thesis because of its simplicity and robustness. There have been many contributions in the SMC field in the last decade. Many existingmethods are available for the SMC design for second-order systems. However, the SMC design becomes extremely complex if the system order increases. Therefore, the first part of this thesis focuses on developing arbitrary-order SMC strategies with a relatively simpler design while providing finite-time convergence. Novel methods are developed with both continuous and discontinuous control structures. The second part of this thesis focuses on developing algorithms to provide even faster convergence speed than that of finite-time convergent algorithms. Some practical applications need strict constraints on time response due to security reasons or to ameliorate the productiveness. For example, a missile or any aerial launch vehicle can be hugely affected by a strong wind gust deviating it from the desired trajectory, thus yielding a significant degree of initial tracking error. It is worth mentioning that the state convergence achieved in SMC during sliding can be either asymptotic or in finite-time, depending on the selection of the surface. Furthermore, it primarily depends on the initial conditions of the states. This provides a motivation to focus on developing SMC controllers where the convergence time does not depend on initial conditions, and a well-defined theoretical analysis is provided in the thesis regarding arbitrary-order fixed-time convergent SMC design. Subsequently, a predefined-time convergent second-order differentiator and observer are proposed. The main advantage of the proposed differentiator is to calculate the derivative of a given signal in fixed-time while the least upper bound of the fixed stabilisation time is equal to a tunable parameter. Similarly, the proposed predefined-time observer is robust with respect to bounded uncertainties and can also be used to estimate the uncertainties. The final part of the thesis is focused on the applications of the proposed algorithms. First of all, a novel third-order SMC is designed for a piezoelectric-driven motion systems achieving better accuracy and control performance. Later on, an experimental validation of the proposed controller is conducted on an induction motor setup. Later, a fixed-time convergent algorithm is proposed for an automatic generation control (AGC) of a multi-area interconnected power system while considering the non-linearities in the dynamic system. The final part is focused on developing fixed-time convergent algorithms in a co-operative environment. The reason for selecting such a system is the presence of the highest degree of uncertainties. To this end, a novel distributed algorithm is developed for achieving second-order consensus in the multiagent systems by designing a full-order fixed-time convergent sliding surface