A large number of power sources, operational in a microgrid, optimum power sharing and accordingly controlling the power sources along with scheduling loads are the biggest challenges in modern power system. In the era of smart grid, the solution is certainly not simple paralleling. Hence it is required to develop a control scheme that delivers the overall power requirements while also adhering to the power limitations of each source. As the penetration of distributed generators increase and are diversifi ed, the choice of decentralized control becomes preferable. In this work, a decentralized control framework is conceived. The primary approach is taken where a small hybrid system is investigated and decentralized control schemes were developed and subsequently tested in a hardware in the loop in conjunction with the hybrid power system setup developed at the laboratory. The control design approach is based on the energy conservation principle. However, considering the vastness of the real power network and its complexity of operation along with the growing demand of smarter grid operations, called for a revamp in the control framework design. Hence, in the later phase of this work, a novel framework is developed based on the coupled dynamical system theory, where each control node corresponds to one distributed generator connected to the microgrid. The coupling topology and coupling strengths of individual nodes are designed to be adjustable. The layer is modeled as a set of coupled differential equations of pre-assigned order. The control scheme adjusts the coupling weights so that steady state constraints are met at the system level, while allowing flexibility to explore the solution space. Additionally, the approach guarantees stable equilibria during power redistribution. The theoretical development is verified using simulations in matlab simulink environment.

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<p>Advanced architectures for cluster-based services that have been recently proposed allow for service differentiation, server overload control and high utilization of resources. These systems, however, rely on centralized functions, which limit their ability to scale and to tolerate faults. In addition, they do not have built-in architectural support for automatic reconfiguration in case of failures or addition/removal of system components.</p><p>Recent research in peer-to-peer systems and distributed management has demonstrated the potential benefits of decentralized over centralized designs: a decentralized design can reduce the configuration complexity of a system and increase its scalability and fault tolerance.</p><p>This research focuses on introducing self-management capabilities into the design of cluster-based services. Its intended benefits are to make service platforms dynamically adapt to the needs of customers and to environment changes, while giving the service providers the capability to adjust operational policies at run-time.</p><p>We have developed a decentralized design that efficiently allocates resources among multiple services inside a server cluster. The design combines the advantages of both centralized and decentralized architectures. It allows associating a set of QoS objectives with each service. In case of overload or failures, the quality of service degrades in a controllable manner. We have evaluated the performance of our design through extensive simulations. The results have been compared with performance characteristics of ideal systems.</p>

Autonomous aerial transportation has multiple potential applications including emergency cases and rescue missions where ground intervention may be difficult. In this context, the following work will address the control of multi-agent Vertical Take-off and Landing aircraft (VTOL) transportation system. We develop a decentralized method. The advantage of such a solution is that it can provide better maneuverability and lifting capabilities compared to existing systems. First, we consider a cooperative group of VTOLs transporting one payload. The main idea is that each agent perceive the interaction with other agents as a disturbance while assuming a negotiated motion model and imposing certain magnitude bounds on each agent. The theoretical model will be then validated using a numerical simulation illustrating the interesting features of the presented control method. Results show that under specified disturbances, the algorithm is able to guarantee the tracking with a minimal error. We describe a toolbox that has been developed for this purpose. Then, a system of multiple VTOLs lifting payloads will be studied. The algorithm assures that the VTOLs are coordinated with minimal communication. Additionally, a novel gripper design for ferrous objects is presented that enables the transportation of ferrous objects without a cable. Finally, we discuss potential connections to human in the loop transportation systems.

This thesis considers the coordinated control of autonomous agents. The agents are modeled as double integrators, one for each Cartesian dimension. The goal is to force the agents to converge to a formation specified by their desired relative positions. To this end a pair of one-step-ahead optimization based control laws are developed. The control algorithms produce a communication topology that mirrors the geometric formation topology due to the careful choice of the minimized cost functions. Through this equivalence a natural understanding of the relationship between the geometric formation topology and the communication infrastructure is gained. It is shown that the control laws are stable and guarantee convergence for all viable formation topologies. Additionally, velocity constraints can be added to allow the formation to follow fixed or arbitrary time dependent velocities. Both control algorithms only require local information exchange. As additional agents attach to the formation, only those agents that share position constraints with the joining agents need to adjust their control laws. When redundancy is incorporated into the formation topology, it is possible for the system to survive loss of agents or communication channels. In the event that an agent drops out of the formation, only the agents with position interdependence on the lost agent need to adjust their control laws. Finally, if a communication channel is lost, only the agents that share that communication channel must adjust their control laws. The first control law falls into the category of distributed control, since it requires either the global information exchange to compute the formation size or an a priori knowledge of the largest possible formation. The algorithm uses the network size to penalize the control input for each formation. When using a priori knowledge, it is shown that additional redundancy not only adds robustness to loss of agents or communication channels, but it also decreases the settling times to the desired formation. Conversely, the overall control strategy suffers from sluggish response when the network is small with respect to the largest possible network. If global information exchange is used, scalability suffers. The second control law was developed to address the negative aspects of the first. It is a fully decentralized controller, as it does not require global information exchange or any a priori knowledge.

This thesis investigates designs that enable individual components of a distributed system to work together and coordinate their actions towards a common goal. While the basic motivation for our research is to develop engineering principles for large-scale autonomous systems, we address the problem in the context of resource management in server clusters that provide web services. To this end, we have developed, implemented and evaluated a decentralized design for resource management that follows four principles. First, in order to facilitate scalability, each node has only partial knowledge of the system. Second, each node can adapt and change its role at runtime. Third, each node runs a number of local control mechanisms independently and asynchronously from its peers. Fourth, each node dynamically adapts its local configuration in order to optimize a global utility function. The design includes three fundamental building blocks: overlay construction, request routing and application placement. Overlay construction organizes the cluster nodes into a single dynamic overlay. Request routing directs service requests towards nodes with available resources. Application placement partitions the cluster resources between applications, and dynamically adjusts the allocation in response to changes in external load, node failures, etc. We have evaluated the design using complexity analysis, simulation and prototype implementation. Using complexity analysis and simulation, we have shown that the system is scalable, operates efficiently in steady state, quickly adapts to external events and allows for effective service differentiation by a system administrator. A prototype has been built using accepted technologies (Java, Tomcat) and evaluated using standard benchmarks (TPC-W and RUBiS). The evaluation results show that the behavior of the prototype matches closely that of the simulated design for key metrics related to adaptability and robustness, therefore validating our design and proving its feasibility. / QC 20100629

This thesis focuses on decentralized deadbeat output regulation of discrete-time nonlinear plants that are composed of multiple agents. These agents interact, via scalar-valued signals, in a known structured way represented with a graph. This work is motivated by applications where it is infeasible and/or undesirable to introduce control action within each plant agent; instead, control agents are introduced to interact with certain plant agents, where each control agent focuses on regulating a specific plant agent, called its target. Then, two analyses are carried out to determine if regulation is achieved: targeting analysis is used to determine if control laws can be found to regulate all target agents, then growing analysis is used to determine the effect of those control laws on non-target plant agents. The strength of this novel approach is the intuitively-appealing notion of each control agent focusing on the regulation of just one plant agent. This work goes beyond previous research by generalizing the class of allowable plant dynamics, considering not only arbitrary propagation times through plant agents, but also allowing for non-symmetrical influence between the agents. Moreover, new necessary and sufficient algebraic conditions are derived to determine when targeting succeeds. The main contribution of this work, however, is the development of new easily-verifiable conditions necessary for targeting and/or growing to succeed. These new conditions are valuable due to their simplicity and scalability to large systems. They concern the positioning of control agents and targets as well as the propagation time of signals through the plant, and they help significantly with design decisions. Various graph structures (such as queues, grids, spiders, rings, etc.) are considered and for each, these conditions are used to develop a control scheme with the minimum number of control agents needed.

Abstract In this dissertation, the eigenvalue analysis and decentralized robust controller design of uncertain multi-time-scale system with parametrical perturbations are considered. Because the eigenvalues of the multi-time-scale systems cluster in some difference regions of the complex plane, we can use the singular perturbation method to separate the systems into some subsystems. These subsystems are independent to each other. We can discuss the properties of eigenvalues and design controller for these subsystem respectively, then we composite these controllers to a decentralized controller. The eigenvalue positions dominate the stability and the performance of the dynamic system. However, we cannot obtain the precise position of the eigenvalues from the influence of parametrical perturbations. The sufficient conditions of the eigenvalues clustering for the multi-time-scale systems will be discussed. The uncertainties consider as unstructured and structured perturbations are taken into considerations. The design algorithm provides for designing a decentralized controller that can assign the poles to our respect regions. The specified regions are half-plane and circular disk. Furthermore, the concepts of decentralized control and optimal control are used to design the linear quadratic regulator (LQR) controller and linear quadratic Gaussian (LQG) controller for the perturbed multi-time-scale systems. That is, the system can get the optimal robust performance. The bound of the singular perturbation parameter would influence the robust stability of the multi-time-scale systems. Finally, the sufficient condition to obtain the upper bound of the singular perturbation parameter presented by the Lyapunov method and matrix norm. The condition also extends for the pole assignment in the specified regions of each subsystem respectively. The illustrative examples are presented behind each topic. They show the applicability of the proposed theorems, and the results are satisfactory.

The objective of this thesis is to develop decentralized methods for building robust multi-agent networks through self-organization. Multi-agent networks appear in a large number of natural and engineered systems, including but not limited to, biological networks, social networks, communication systems, transportation systems, power grids, and robotic swarms. Networked systems typically consist of numerous components that interact with each other to achieve some collaborative tasks such as flocking, coverage optimization, load balancing, or distributed estimation, to name a few. Multi-agent networks are often modeled via interaction graphs, where the nodes represent the agents and the edges denote direct interactions between the corresponding agents. Interaction graphs play a significant role in the overall behavior and performance of multi-agent networks. There- fore, graph theoretic analysis of networked systems has received a considerable amount of attention within the last decade. In many applications, network components are likely to face various functional or structural disturbances including, but not limited to, component failures, noise, or malicious attacks. Hence, a desirable network property is robustness, which is the ability to perform reasonably well even when the network is subjected to such perturbations. In this thesis, robustness in multi-agent networks is pursued in two parts. The first part presents a decentralized graph reconfiguration scheme for formation of robust interaction graphs. Particularly, the proposed scheme transforms any interaction graph into a random regular graph, which is robust to the perturbations of their nodes/links. The second part presents a decentralized coverage control scheme for optimal protection of networks by some mobile security resources. As such, the proposed scheme drives a group of arbitrarily deployed resources to optimal locations on a network in a decentralized fashion.

In a multi-agent system (MAS), the overall performance is greatly influenced by both the design and the control of the agents. The physical design determines the agent capabilities, and the control strategies drive the agents to pursue their objectives using the available capabilities. The objective of this thesis is to incorporate control strategies in the early conceptual design of an MAS. As such, this thesis proposes a methodology that mainly explores the interdependency between the design variables of the agents and the control strategies used by the agents. The output of the proposed methodology, i.e. the interdependency between the design variables and the control strategies, can be utilized in the requirement analysis as well as in the later design stages to optimize the overall system through some higher fidelity analyses. In this thesis, the proposed methodology is applied to a persistent multi-UAV surveillance problem, whose objective is to increase the situational awareness of a base that receives some instantaneous monitoring information from a group of UAVs. Each UAV has a limited energy capacity and a limited communication range. Accordingly, the connectivity of the communication network becomes essential for the information flow from the UAVs to the base. In long-run missions, the UAVs need to return to the base for refueling with certain frequencies depending on their endurance. Whenever a UAV leaves the surveillance area, the remaining UAVs may need relocation to mitigate the impact of its absence. In the control part of this thesis, a set of energy-aware control strategies are developed for efficient multi-UAV surveillance operations. To this end, this thesis first proposes a decentralized strategy to recover the connectivity of the communication network. Second, it presents two return policies for UAVs to achieve energy-aware persistent surveillance. In the design part of this thesis, a design space exploration is performed to investigate the overall performance by varying a set of design variables and the candidate control strategies. Overall, it is shown that a control strategy used by an MAS affects the influence of the design variables on the mission performance. Furthermore, the proposed methodology identifies the preferable pairs of design variables and control strategies through low fidelity analysis in the early design stages.