Decentralized Hybrid Formation Control of Unmanned Aerial Vehicles

This paper presents a decentralized hybrid supervisory control approach for a team of unmanned helicopters that are involved in a leader-follower formation mission. Using a polar partitioning technique, the motion dynamics of the follower helicopters are abstracted to finite state machines. Then, a discrete supervisor is designed in a modular way for different components of the formation mission including reaching the formation, keeping the formation, and collision avoidance. Furthermore, a formal technique is developed to design the local supervisors decentralizedly, so that the team of helicopters as whole, can cooperatively accomplish a collision-free formation task

The Role of Human-Automation Consensus in Multiple Unmanned Vehicle Scheduling

Objective: This study examined the impact of increasing automation replanning rates on operator performance and workload when supervising a decentralized network of heterogeneous unmanned vehicles. Background: Futuristic unmanned vehicles systems will invert the operator-to-vehicle ratio so that one operator can control multiple dissimilar vehicles connected through a decentralized network. Significant human-automation collaboration will be needed because of automation brittleness, but such collaboration could cause high workload. Method: Three increasing levels of replanning were tested on an existing multiple unmanned vehicle simulation environment that leverages decentralized algorithms for vehicle routing and task allocation in conjunction with human supervision. Results: Rapid replanning can cause high operator workload, ultimately resulting in poorer overall system performance. Poor performance was associated with a lack of operator consensus for when to accept the automation’s suggested prompts for new plan consideration as well as negative attitudes toward unmanned aerial vehicles in general. Participants with video game experience tended to collaborate more with the automation, which resulted in better performance. Conclusion: In decentralized unmanned vehicle networks, operators who ignore the automation’s requests for new plan consideration and impose rapid replans both increase their own workload and reduce the ability of the vehicle network to operate at its maximum capacity. Application: These findings have implications for personnel selection and training for futuristic systems involving human collaboration with decentralized algorithms embedded in networks of autonomous systems.Aurora Flight Sciences Corp.United States. Office of Naval Researc

Correct-By-Construction Control Synthesis for Systems with Disturbance and Uncertainty

This dissertation focuses on correct-by-construction control synthesis for Cyber-Physical Systems (CPS) under model uncertainty and disturbance. CPSs are systems that interact with the physical world and perform complicated dynamic tasks where safety is often the overriding factor. Correct-by-construction control synthesis is a concept that provides formal performance guarantees to closed-loop systems by rigorous mathematic reasoning. Since CPSs interact with the environment, disturbance and modeling uncertainty are critical to the success of the control synthesis. Disturbance and uncertainty may come from a variety of sources, such as exogenous disturbance, the disturbance caused by co-existing controllers and modeling uncertainty. To better accommodate the different types of disturbance and uncertainty, the verification and control synthesis methods must be chosen accordingly. Four approaches are included in this dissertation. First, to deal with exogenous disturbance, a polar algorithm is developed to compute an avoidable set for obstacle avoidance. Second, a supervised learning based method is proposed to design a good student controller that has safety built-in and rarely triggers the intervention of the supervisory controller, thus targeting the design of the student controller. Third, to deal with the disturbance caused by co-existing controllers, a Lyapunov verification method is proposed to formally verify the safety of coexisting controllers while respecting the confidentiality requirement. Finally, a data-driven approach is proposed to deal with model uncertainty. A minimal robust control invariant set is computed for an uncertain dynamic system without a given model by first identifying the set of admissible models and then simultaneously computing the invariant set while selecting the optimal model. The proposed methods are applicable to many real-world applications and reflect the notion of using the structure of the system to achieve performance guarantees without being overly conservative.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145933/1/chenyx_1.pd

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

On the Control of Microgrids Against Cyber-Attacks: A Review of Methods and Applications

Nowadays, the use of renewable generations, energy storage systems (ESSs) and microgrids (MGs) has been developed due to better controllability of distributed energy resources (DERs) as well as their cost-effective and emission-aware operation. The development of MGs as well as the use of hierarchical control has led to data transmission in the communication platform. As a result, the expansion of communication infrastructure has made MGs as cyber-physical systems (CPSs) vulnerable to cyber-attacks (CAs). Accordingly, prevention, detection and isolation of CAs during proper control of MGs is essential. In this paper, a comprehensive review on the control strategies of microgrids against CAs and its defense mechanisms has been done. The general structure of the paper is as follows: firstly, MGs operational conditions, i.e., the secure or insecure mode of the physical and cyber layers are investigated and the appropriate control to return to a safer mode are presented. Then, the common MGs communication system is described which is generally used for multi-agent systems (MASs). Also, classification of CAs in MGs has been reviewed. Afterwards, a comprehensive survey of available researches in the field of prevention, detection and isolation of CA and MG control against CA are summarized. Finally, future trends in this context are clarified

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