1,721,022 research outputs found
Performanz-Orientierter Entwurf verteilter Regelungsgesetze für vernetzte Systeme
No full textThis dissertation focuses on the design of distributed control for interconnected systems by taking into account the communication topology and the information that needs to be exchanged. The contributions of this dissertation are summarized as follows. First, a novel two-layer control architecture is proposed that allows to jointly improve the system performance and guarantee its stability under permanent communication link failures. Next, explicit solutions for topology design of distributed control are investigated for the first time. Furthermore, an algorithm is developed to design distributed control in the absence of global model information. Finally, an approach to design distributed control for a coverage control problem which guarantees that the system avoids undesired local optima is proposed.Diese Dissertation behandelt den Entwurf von verteilten
Regelungsgesetzen für vernetzte Systeme unter Berücksichtigung der
Kommunikationstopologie und der ausgetauschten Information. Die Beiträge
dieser Dissertation lassen wie folgt zusammenfassen: Zunächst wird eine
neuartige zweischichtige Regelungsarchitektur vorgestellt, welche die
Regelgüte verbessert und die Stabilität auch unter andauerndem Ausfall
der Kommunikationsverbindungen garantiert. Anschließend werden zum
ersten Mal explizite Lösungen für den Topologieentwurf in verteilten
Regelungsszstemen vorgestellt und diskutiert. Darüberhinaus wird ein
Algorithmus zum Entwurf verteilter Regelgesetze in Abwesenheit
vollständiger Modellinformation hergeleitet. Schließlich wird ein
innovatives Verfahren zum Entwurf verteilter Regelungsgesetze in einem
”Coverage”-Problem vorgestellt mit dem unerwünschte lokale Optima
garantiert vermieden werden
Distributed Data-Driven Algorithms for Eigen-Analysis of Power System Models
No full textThis paper addresses the monitoring of inter-area oscillations, which pose significant risks to power system’s stability. We present distributed data-driven algorithms to estimate inter-area oscillation modes and their corresponding left and right eigenvectors. Specifically, the power system is divided into coherent areas with local estimators that process real-time PMU data. The averaged state information are exchanged over a strongly connected communication network to distributively estimate the eigenvalues of the reduced order model. By using the estimated eigenvalues and leveraging the structure of the solution to the reduced-order dynamical model, the eigenvectors are then computed via solving a least square problem in a distributed manner. Simulations conducted on a 50-bus power system model comprising four areas demonstrate that the algorithm offers a scalable and effective solution for eigenanalysis in power systems.Peer reviewe
Performance-Oriented Communication Topology Design For Distributed Control Of Interconnected Systems
No full textCommunication networks provide a larger flexibility for the control design of interconnected systems by allowing the information exchange between the local controllers of the subsystems which can be used to improve the overall system performance. However, the interconnected systems may become unstable due to permanent communication link failures. This article presents a novel two-layer control architecture that allows to jointly improve the system performance which is the decay rate and guarantee the stability of the interconnected system under permanent communication link failures. As a novelty, the design of communication topology between the local controllers is also taken into account. On the other hand, it is still not well understood how significant the role of each possible communication link is in improving the system performance. Another novelty of this article is thus to propose a method based on eigenvalue sensitivity analysis in order to characterize the influence of each possible communication link in improving the performance of the overall system. In addition, for a special class of systems and physical interconnection topology, explicit solutions on communication topology design are derived for the first time. The solutions provide some insights into how the heterogeneity of the subsystem local dynamics, the strength of interconnection and the size of the network affect the optimal communication topology. © 2013 Springer-Verlag London
Connectivity-preserving distributed algorithms for removing links in directed networks
No full textThis article considers the link removal problem in a strongly connected directed network with the goal of minimizing the dominant eigenvalue of the network’s adjacency matrix while maintaining its strong connectivity. Due to the complexity of the problem, this article focuses on computing a suboptimal solution. Furthermore, it is assumed that the knowledge of the overall network topology is not available. This calls for distributed algorithms which rely solely on the local information available to each individual node and information exchange between each node and its neighbors. Two different strategies based on matrix perturbation analysis are presented, namely simultaneous and iterative link removal strategies. Key ingredients in implementing both strategies include novel distributed algorithms for estimating the dominant eigenvectors of an adjacency matrix and for verifying strong connectivity of a directed network under link removal. It is shown via numerical simulations on different type of networks that in general the iterative link removal strategy yields a better suboptimal solution. However, it comes at a price of higher communication cost in comparison to the simultaneous link removal strategy.Peer reviewe
Distributed Algorithm for Link Removal in Directed Networks
No full textThis paper considers the problem of removing a fraction of links from a strongly connected directed network such that the largest (in module) eigenvalue of the adjacency matrix corresponding to the network structure is minimized. Due to the complexity of the problem, an effective and scalable algorithm based on eigenvalue sensitivity analysis is proposed in the literature to compute the suboptimal solution to the problem. However, the algorithm requires knowledge of the global network structure and does not preserve strong connectivity of the resulting network. This paper proposes distributed algorithms which allow distributed implementation of the previously mentioned algorithm by relying solely on local information on the network topology while guaranteeing strong connectivity of the resulting network. A numerical example is provided to demonstrate the proposed distributed algorithm.Peer reviewe
Resilient and Privacy-Preserving Leader-Follower Consensus in Presence of Cyber-Attacks
No full textThis letter presents a novel and unified distributed control framework that ensures resilient leader-follower consensus in the presence of unknown but bounded attacks on both the communication network and actuators while simultaneously preserving the privacy of an agent’s physical state from eavesdroppers. To this end, a virtual state and time-varying signals are introduced to each agent, which function as masks to the physical state and are designed to ensure resilient leader-follower consensus. The proposed control strategy does not require high network connectivity and imposes no restrictions on the number of attacks. A numerical example is provided to illustrate the results.Peer reviewe
A Distributed Algorithm to Establish Strong Connectivity in Spatially Distributed Networks via Estimation of Strongly Connected Components
No full textThis paper presents a distributed algorithm forensuring the strong connectivity of spatially distributed networkswhere the communication network topology depends onboth the position and communication range of the nodes. This isachieved by adding new links via adjusting the communicationrange and/or controlling the position of the nodes. The distributedalgorithms rely on the estimation of strongly connectedcomponents of a dynamic network topology, accomplishedthrough the utilization of the maximum consensus algorithm.The proposed strategies are scalable and converge in a finitenumber of steps without requiring information on the overallnetwork topology. Finally, the proposed distributed algorithmis demonstrated through two case studies of ensuring strongconnectivity in wireless networks with static and mobile nodes.Peer reviewe
Finite-Time Distributed Algorithms for Verifying and Ensuring Strong Connectivity of Directed Networks
No full textThe strong connectivity of a directed graph associated with the communication network topology is crucial in ensuring the convergence of many distributed estimation/control/optimization algorithms. However, the assumption on the network's strong connectivity may not always be satisfied in practice. In addition, information on the overall network topology is often not available, e.g., due to privacy concerns or geographical constraints which calls for a distributed algorithm. This paper aims to fill a crucial gap in the literature due to the absence of a fully distributed algorithm to verify and ensure in finite-time the strong connectivity of a directed network. Specifically, inspired by the maximum consensus algorithm we propose distributed algorithms that enable individual node in a networked system to verify the strong connectivity of a directed graph and further, if necessary, augment a minimum number of new links to ensure the directed graph's strong connectivity. The proposed distributed algorithms are implemented without requiring information of the overall network topology and are scalable as they only require finite storage and converge in finite number of steps. Furthermore, the algorithms also preserve the privacy in terms of the overall network's topology. Finally, the proposed distributed algorithms are demonstrated and evaluated via numerical results.Peer reviewe
Data-Driven Distributed Algorithms for Estimating Eigenvalues and Eigenvectors of Interconnected Dynamical Systems
No full textThe paper presents data-driven algorithms to estimate in a distributed manner the eigenvalues, right and left eigenvectors of an unknown linear (or linearized) interconnected dynamic system. In particular, the proposed algorithms do not require the identification of the system model in advance before performing the estimation. As a first step, we consider interconnected dynamical system with distinct eigenvalues. The proposed strategy first estimates the eigenvalues using the well-known Prony method. The right and left eigenvectors are then estimated by solving distributively a set of linear equations. One important feature of the proposed algorithms is that the topology of communication network used to perform the distributed estimation can be chosen arbitrarily, given that it is connected, and is also independent of the structure or sparsity of the system (state) matrix. The proposed distributed algorithms are demonstrated via a numerical example.Peer reviewe
Cooperative Systems in Presence of Cyber-Attacks: A Unified Framework for Resilient Control and Attack Identification
No full textThis paper considers a cooperative control problem in presence of unknown attacks. The attacker aims at destabilizing the consensus dynamics by intercepting the system’s communication network and corrupting its local state feedback. We first revisit the virtual network based resilient control proposed in our previous work and provide a new interpretation and insights into its implementation. Based on these insights, a novel distributed algorithm is presented to detect and identify the compromised communication links. It is shown that it is not possible for the adversary to launch a harmful and stealthy attack by only manipulating the physical states being exchanged via the network. In addition, a new virtual network is proposed which makes it more difficult for the adversary to launch a stealthy attack even though it is also able to manipulate information being exchanged via the virtual network. A numerical example demonstrates that the proposed control framework achieves simultaneously resilient operation and real-time attack identification.Peer reviewe
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