Designing local controllers for networked systems is challenging, because in these systems each local controller can often access only part of the overall information on system parameters and sensor measurements. Traditional control design cannot be easily applied due to the unconventional information patterns, communication network imperfections, and design procedure complexities. How to control large-scale systems is of immediate societal importance as they appear in many emerging applications, such as intelligent transportation systems, smart grids, and energy-efficient buildings. In this thesis, we make three contributions to the problem of designing networked controller under information asymmetries and limitations. In the first contribution, we investigate how to design local controllers to optimize a cost function using only partial knowledge of the model governing the system. Specifically, we derive some fundamental limitations in the closed-loop performance when the design of each controller only relies on local plant model information. Results are characterized in the structure of the networked system as well as in the available model information. Both deterministic and stochastic formulations are considered for the closed-loop performance and the available information. In the second contribution of the thesis, we study decision making in transportation systems using heterogeneous routing and congestion games. It is shown that a desirable global behavior can emerge from simple local strategies used by the drivers to choose departure times and routes. Finally, the third contribution is a novel stochastic sensor scheduling policy for ad-hoc networked systems, where a varying number of control loops are active at any given time. It is shown that the policy provides stochastic guarantees for the network resources dynamically allocated to each loop. / <p>QC 20140221</p>

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: One potential application of multi-robot systems is collective transport, a task in which multiple mobile robots collaboratively transport a payload that is too large or heavy to be carried by a single robot. Numerous control schemes have been proposed for collective transport in environments where robots can localize themselves (e.g., using GPS) and communicate with one another, have information about the payload's geometric and dynamical properties, and follow predefined robot and/or payload trajectories. However, these approaches cannot be applied in uncertain environments where robots do not have reliable communication and GPS and lack information about the payload. These conditions characterize a variety of applications, including construction, mining, assembly in space and underwater, search-and-rescue, and disaster response. Toward this end, this thesis presents decentralized control strategies for collective transport by robots that regulate their actions using only their local sensor measurements and minimal prior information. These strategies can be implemented on robots that have limited or absent localization capabilities, do not explicitly exchange information, and are not assigned predefined trajectories. The controllers are developed for collective transport over planar surfaces, but can be extended to three-dimensional environments. This thesis addresses the above problem for two control objectives. First, decentralized controllers are proposed for velocity control of collective transport, in which the robots must transport a payload at a constant velocity through an unbounded domain that may contain strictly convex obstacles. The robots are provided only with the target transport velocity, and they do not have global localization or prior information about any obstacles in the environment. Second, decentralized controllers are proposed for position control of collective transport, in which the robots must transport a payload to a target position through a bounded or unbounded domain that may contain convex obstacles. The robots are subject to the same constraints as in the velocity control scenario, except that they are assumed to have global localization. Theoretical guarantees for successful execution of the task are derived using techniques from nonlinear control theory, and it is shown through simulations and physical robot experiments that the transport objectives are achieved with the proposed controllers. / Dissertation/Thesis / Doctoral Dissertation Mechanical Engineering 2020

Collective robotic behavior poses significant advantages over classical control methods such as system response and robustness. Biological cooperative communities have provided great insights for development of many control algorithms. Localized chemical signaling within bacterial communities is used for directed movement and dynamic density measurements. Both individual and population scale models have been created to adequately model community dynamics. These dynamics, including directed motion due to chemotaxis and density controlled functionality from quorum sensing, are modeled through an individual scale in a community scale environment. This modeling provides both a platform for analyzing the BacteriaBot engineered system as well as inspires decentralized stochastic control techniques for solving bacteria-like collaborative control problems. / Master of Science

No description available.

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. 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. 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. 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. / QC 20101123

As we begin to analyze and construct extremely complex systems, a theory for understanding and designing the underlying architecture becomes very important. To move in the direction of a precise theory of architecture, this thesis will provide some concrete tools to analyze and design complex systems with a given network structure. The first main result of this thesis analyzes the vulnerability of a system and shows that a system's vulnerability depends on its network structure. We will consider destabilization attacks acting on a single link in a system's logical interconnection structure. The concept of a vulnerable link is characterized and necessary and sufficient conditions for identifying these links are provided. The vulnerability of various system architectures are then characterized by the vulnerability of their weakest link, and it is shown that every transfer function has a completely secure architecture with no vulnerable links. The second part of this thesis focuses on synthesizing controllers with a specified network structure. It presents a new approach to distributed controller design that exploits the dynamical structure function representation of linear time invariant systems to characterize the structure of a system. The design technique sequentially constructs each link in an arbitrary controller signal structure, and the main theorem proves that either the resulting controller is stabilizing or that no controller with the desired structure can stabilize the system.

In this work, a robust decentralized model predictive control regime for a team of cooperating robot systems is designed. Their assumed dynamics are in continuous time and non-linear. The problem involves agents whose dynamics are independent of one-another, and its solution couples their constraints as a means of capturing the cooperative behaviour required. Analytical proofs are given to show that, under the proposed control regime: (a) Subject to initial feasibility, the optimization solved at each step by each agent will always be feasible, irrespective of whether or not disturbances affect the agents. In the former case, recursive feasibility is established through successive restriction of each agent's constraints during the periodic solution to its respective optimization problem. (b) Each (sub)system can be stabilized to a desired configuration, either asymptotically when uncertainty is absent, or within a neighbourhood of it, when uncertainty is present, thus attenuating the affecting disturbance. In this context, disturbances are assumed to be additive and bounded. Simulations verify the efficacy of the proposed method over a range of different operating environments. / I detta arbete, en robust decentraliserad modell prediktiv kontroll regime förett lag av samverkande robotsystem är utformade. Deras antagnat dynamikär i kontinuerlig tid och olinjär. Problemet involverar agenter vars dynamik äroberoende av varandra, och sina lösning kopplar sina begränsningar som ettmedel för att fånga det kooperativa beteendet som krävs. Analytiska bevis gesför att visa att, enligt det föreslagna kontrollsystemet: (a) med förbehåll förförsta genomförbarhet, kommer optimeringen som löses vid varje steg av varjeagent alltid vara möjligt, oavsett huruvida störningar påverkar agenserna ellerinte. I det förre fallet är rekursiv genomförbarhet etablerad genom successivabegränsningar av varje agents inskränkning under den periodiska lösningentill dess respektive optimeringsproblem. (b) Varje (sub) system kan stabiliserastill en önskad konfiguration, antingen asymptotiskt när osäkerhet saknas,eller inom en närhet av det, när osäkerhet är närvarande, således dämparpåverkanstörning. I detta sammanhang antas störningar vara additiv och avgränsas.Simuleringar verifierar effekten av den föreslagna metoden över ettintervall av olika driftsmiljöer.

In this dissertation, we consider differential games for multi-agent systems under distributed information where every agent is only able to acquire information about the others according to a directed information graph of local communication/sensor networks. Such games arise naturally from many applications including mobile robot coordination, power system optimization, multiplayer pursuit-evasion games, etc. Since the admissible strategy of each agent has to conform to the information graph constraint, the conventional game strategy design approaches based upon Riccati equation(s) are not applicable because all the agents are required to have the information of the entire system. Accordingly, the game strategy design under distributed information is commonly known to be challenging. Toward this end, we propose novel open-loop and feedback game strategy design approaches for Nash equilibrium and noninferior solutions with a focus on linear quadratic differential games. For the open-loop design, approximate Nash/noninferior game strategies are proposed by integrating distributed state estimation into the open-loop global-information Nash/noninferior strategies such that, without global information, the distributed game strategies can be made arbitrarily close to and asymptotically converge over time to the global-information strategies. For the feedback design, we propose the best achievable performance indices based approach under which the distributed strategies form a Nash equilibrium or noninferior solution with respect to a set of performance indices that are the closest to the original indices. This approach overcomes two issues in the classical optimal output feedback approach: the simultaneous optimization and initial state dependence. The proposed open-loop and feedback design approaches are applied to an unmanned aerial vehicle formation control problem and a multi-pursuer single-evader differential game problem, respectively. Simulation results of several scenarios are presented for illustration.

In decentralized control, agents have only a partial view and partial control of the system and must cooperate to achieve the control objective. In order to synthesize a decentralized control solution, a specification must satisfy the co-observability property. Existing co-observability verification methods require the possibly intractable construction of the complete system. To address this issue, we introduce an incremental verification of co-observability approach. Selected subgroups of the system are evaluated individually, until verification is complete. The new method is potentially much more efficient than the monolithic approaches, in particular for systems composed of many subsystems, allowing for some intractable problems to be manageable. Properties of this new strategy are presented, along with a corresponding algorithm and an example. To further increase the scalability of decentralized control, we wish to adapt the existing Hierarchical Interface-Based Supervisory Control (HISC) to support it. We introduce the Hierarchical Interface-Based Decentralized Supervisory Control (HIDSC) framework that extends HISC to decentralized control. To adapt co-observability for HIDSC, we propose a per-component definition of co-observability along with a verification strategy that requires only a single component at a time in order to verify co-observability. Finally, we provide and prove the necessary and sufficient conditions for supervisory control existence in the HIDSC framework and illustrate our approach with an example. As the entire system model never needs to be constructed, HIDSC potentially provides significant savings. / Thesis / Doctor of Philosophy (PhD)