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The problem of distributed state estimation over a sensor network in which a set of nodes collaboratively estimates the state of continuous-time linear systems is considered. Distributed estimation strategies improve estimation and robustness of the sensors to environmental obstacles and sensor failures in a sensor network. In particular, this dissertation focuses on the benefits of weight adaptation of the interconnection gains in distributed Kalman filters, distributed unknown input observers, and distributed functional observers. To this end, an adaptation strategy is proposed with the adaptive laws derived via a Lyapunov-redesign approach. The justification for the gain adaptation stems from a desire to adapt the pairwise difference of estimates as a function of their agreement, thereby enforcing an interconnection-dependent gain. In the proposed scheme, an adaptive gain for each pairwise difference of the interconnection terms is used in order to address edge-dependent differences in the estimates. Accounting for node-specific differences, a special case of the scheme is presented where it uses a single adaptive gain in each node estimate and which uniformly penalizes all pairwise differences of estimates in the interconnection term. In the case of distributed Kalman filters, the filter gains can be designed either by standard Kalman or Luenberger observers to construct the adaptive distributed Kalman filter or adaptive distributed Luenberger observer. Stability of the schemes has been shown and it is independent of the graph topology and therefore the schemes are applicable to both directed and undirected graphs. The proposed algorithms offer a significant reduction in communication costs associated with information flow by the nodes compared to other distributed Kalman filters. Finally, numerical studies are presented to illustrate the performance and effectiveness of the proposed adaptive distributed Kalman filters, adaptive distributed unknown input observers, and adaptive distributed functional observers.

In this dissertation, new model reference adaptive control architectures are presented with stability, performance, and robustness considerations, to address challenges related to the verification of adaptive control systems. The challenges associated with the transient performance of adaptive control systems is first addressed using two new approaches that improve the transient performance. Specifically, the first approach is predicated on a novel controller architecture, which involves added terms in the update law entitled artificial basis functions. These terms are constructed through a gradient optimization procedure to minimize the system error between an uncertain dynamical system and a given reference model during the learning phase of an adaptive controller. The second approach is an extension of the first one and minimizes the effect of the system uncertainties more directly in the transient phase. In addition, this approach uses a varying gain to enforce performance bounds on the system error and is further generalized to adaptive control laws with nonlinear reference models. Another challenge in adaptive control systems is to achieve system stability and a prescribed level performance in the presence of actuator dynamics. It is well-known that if the actuator dynamics do not have sufficiently high bandwidth, their presence cannot be practically neglected in the design since they limit the achievable stability of adaptive control laws. Another major contribution of this dissertation is to address this challenge. In particular, first a linear matrix inequalities-based hedging approach is proposed, where this approach modifies the ideal reference model dynamics to allow for correct adaptation that is not affected by the presence of actuator dynamics. The stability limits of this approach are computed using linear matrix inequalities revealing the fundamental stability interplay between the parameters of the actuator dynamics and the allowable system uncertainties. In addition, these computations are used to provide a depiction of the feasible region of the actuator parameters such that the robustness to variation in the parameters is addressed. Furthermore, the convergence properties of the modified reference model to the ideal reference model are analyzed. Generalizations and applications of the proposed approach are then provided. Finally, to improve upon this linear matrix inequalities-based hedging approach a new adaptive control architecture using expanded reference models is proposed. It is shown that the expanded reference model trajectories more closely follow the trajectories of the ideal reference model as compared to the hedging approach and through the augmentation of a command governor architecture, asymptotic convergence to the ideal reference model can be guaranteed. To provide additional robustness against possible uncertainties in the actuator bandwidths an estimation of the actuator bandwidths is incorporated. Lastly, the challenge presented by the unknown physical interconnection of large-scale modular systems is addressed. First a decentralized adaptive architecture is proposed in an active-passive modular framework. Specifically, this architecture is based on a set-theoretic model reference adaptive control approach that allows for command following of the active module in the presence of module-level system uncertainties and unknown physical interconnections between both active and passive modules. The key feature of this framework allows the system error trajectories of the active modules to be contained within apriori, user-defined compact sets, thereby enforcing strict performance guarantees. This architecture is then extended such that performance guarantees are enforced on not only the actuated portion (active module) of the interconnected dynamics but also the unactuated portion (passive module). For each proposed adaptive control architecture, a system theoretic approach is included to analyze the closed-loop stability properties using tools from Lyapunov stability, linear matrix inequalities, and matrix mathematics. Finally, illustrative numerical examples are included to elucidate the proposed approaches.

Currently, major power systems almost invariably operate under interconnected conditions to transfer power in a stable and reliable manner. Multi-area reliability evaluation has thus become an invaluable tool in the planning and operation of such systems. Multi - area reliability evaluation is typically done by considering equivalent tie lines between different areas in an integrated power system. It gives approximate results for the reliability indices of a power system as it models each of the areas as a single node to which are connected the entire area generation and loads. The intratransmission lines are only indirectly modeled during the calculation of equivalent tie lines' capacities. This method is very widely used in the power industry, but the influence of the various approximations and assumptions, which are incorporated in this method, on reliability calculations has not been explored. The objective of the research work presented in this thesis is the development of a new method called Composite system based multi - area reliability model, which does multi - area reliability evaluation considering the whole composite system. It models the transmission system in detail and also takes into account the loss sharing policy within an area and no - load loss sharing policy among the areas. The proposed method is applied to standard IEEE 24 bus Reliability Test System (RTS) and the traditional equivalent tie-line method is applied to the multi-area configuration of the same test system. The results obtained by both the methods are analyzed and compared. It is found that the traditional model, although having some advantages, may not give accurate results.

Embedded systems have been present in our daily lives for some time, but trends clearly show a rise in inter-connectivity in such devices. This presents promising new applications and possibilities, but also opens up a lot attack surface. Our goal in this thesis is to find out how you can develop such interconnected embedded systems in a way that guarantees the three major components of information security: Confidentialy, Integrity and Availability. The main focus of security is networked security. In this thesis, a dual approach is taken: investigate the development process of building secure systems, and perform such an implementation. The artifacts produced as byproducts, the software itself, deployment instructions and lessons learned are all presented. It is shown that the process used helps businesses find a somewhat deterministic approach to security, have a higher level of confidence, helps justify the costs that security work entails and helps in seeing security as a business decision. Embedded systems were also shown to present unforeseen obstacles, such as how the lack of a motherboard battery clashes with X.509. In the end, a discussion is made about how far the system can guarantee information security, what problems still exist and what could be done to mitigate them.

As more industrial processes, transportation and appliances have been automated or equipped with some level of artificial intelligence, the number and scale of interconnected systems has grown in the recent past. This is a development which can be expected to continue and therefore the research in performance of interconnected systems and networks is growing. Due to increased automation and sheer scale of networks, dynamically scaling networks is an increasing field and research into scalable performance measures is advancing. Recently, the notion gamma-robustness, a scalable network performance measure, was introduced as a measurement of interconnected systems robustness with respect to external disturbances. This thesis aims to investigate how the distribution of control effort and cost, within interconnected system, affects network performance, measured with gamma-robustness. Further, we introduce a notion of fairness and a measurement of unfairness in order to quantify the distribution of network properties and performance. With these in place, we also present distributed algorithms with which the distribution of control effort can be controlled in order to achieve a desired network performance. We close with some examples to show the strengths and weaknesses of the presented algorithms. / I och med att fler och fler system och enheter blir utrustade med olika grader av intelligens så växer både förekomsten och omfattningen av sammankopplade system, även kallat Multi-Agent Systems. Sådana system kan vi se exempel på i traffikledningssystem, styrning av elektriska nätverk och fordonståg, vi kan också hitta fler och fler exempel på så kallade sensornätverk i och med att Internet of Things och Industry 4.0 används och utvecklas mer och mer. Det som särskiljer sammankopplade system från mer traditionella system med flera olika styrsignaler och utsignaler är att dem sammankopplade systemen inte styrs från en central styrenhet. Istället styrs dem sammankopplade systemen på ett distribuerat sätt i och med att varje agent styr sig själv och kan även ha individuella mål som den försöker uppfylla. Det här gör att analysen av sammankopplade system försvåras, men tidigare forskning har hittat olika regler och förhållninssätt för agenterna och deras sammankoppling för att uppfylla olika krav, såsom stabilitet och robusthet. Men även om dem sammankopplade systemen är både robusta och stabila så kan dem ha egenskaper som vi vill kunna kontrollera ytterligare. Specifikt kan ett sådant prestandamått vara systemens motståndskraft mot påverkan av yttre störningar och i vanliga olänkade system finns det en inneboende avvägning mellan kostnad på styrsignaler och resiliens mot yttre störningar. Samma avvägning hittar vi i sammankopplade system, men i dessa system hittar vi också ytterligare en dimension på detta problem. I och med att ett visst mått av en nätverksprestanda inte nödvändigtvis betyder att varje agent i nätverket delar samma mått kan agenterna i ett nätverk ha olika utväxling mellan styrsignalskostnad och resiliens mot yttre störningar. Detta gör att vissa agenter kan ha onödigt höga styrsignalskonstander, i den mening att systemen skulle uppnå samma nätverksprestanda men med lägre styrsignalskostnad om flera av agenterna skulle vikta om sina kontrollinsatser. I det här examensarbetet har vi studerat hur olika val av kontrollinsats påverkar ett sammankopplat systems prestanda. Vi har gjort detta för att undersöka hur autonoma, men sammankopplade, agenter kan ändra sin kontrollinsats, men med bibehållen nätverksprestanda, och på det sättet minska sina kontrollkostnader. Detta har bland annat resulterat i en distruberad algoritm för att manipulera agenternas kontrollinsats så att skillnaderna mellan agenternas resiliens mot yttre störningar minskar och nätverksprestandan ökar. Vi avslutar rapporten med att visa ett par exempel på hur system anpassade med hjälp av den framtagna algoritmen får ökad prestanda. Avslutningsvis följer en diskussion kring hur vissa antaganden kring systemstruktur kan släppas upp, samt kring vilka områden framtida forskning skulle kunna fortsätta med.

This dissertation studies the model reduction and distributed control problems for interconnected systems, i.e., systems that consist of multiple interacting agents/subsystems. The study of the analysis and synthesis problems for interconnected systems is motivated by the multiple applications that can benefit from the design and implementation of distributed controllers. These applications include automated highway systems and formation flight of unmanned aircraft systems. The systems of interest are modeled using arbitrary directed graphs, where the subsystems correspond to the nodes, and the interconnections between the subsystems are described using the directed edges. In addition to the states of the subsystems, the adopted frameworks also model the interconnections between the subsystems as spatial states. Each agent/subsystem is assumed to have its own actuating and sensing capabilities. These capabilities are leveraged in order to design a controller subsystem for each plant subsystem. In the distributed control paradigm, the controller subsystems interact over the same interconnection structure as the plant subsystems. The models assumed for the subsystems are linear time-varying or linear parameter-varying. Linear time-varying models are useful for describing nonlinear equations that are linearized about prespecified trajectories, and linear parameter-varying models allow for capturing the nonlinearities of the agents, while still being amenable to control using linear techniques. It is clear from the above description that the size of the model for an interconnected system increases with the number of subsystems and the complexity of the interconnection structure. This motivates the development of model reduction techniques to rigorously reduce the size of the given model. In particular, this dissertation presents structure-preserving techniques for model reduction, i.e., techniques that guarantee that the interpretation of each state is retained in the reduced order system. Namely, the sought reduced order system is an interconnected system formed by reduced order subsystems that are interconnected over the same interconnection structure as that of the full order system. Model reduction is important for reducing the computational complexity of the system analysis and control synthesis problems. In this dissertation, interior point methods are extensively used for solving the semidefinite programming problems that arise in analysis and synthesis. / Ph. D. / The work in this dissertation is motivated by the numerous applications in which multiple agents interact and cooperate to perform a coordinated task. Examples of such applications include automated highway systems and formation flight of unmanned aircraft systems. For instance, one can think of the hazardous conditions created by a fire in a building and the benefits of using multiple interacting multirotors to deal with this emergency situation and reduce the risks on humans. This dissertation develops mathematical tools for studying and dealing with these complex systems. Namely, it is shown how controllers can be designed to ensure that such systems perform in the desired way, and how the models that describe the systems of interest can be systematically simplified to facilitate performing the tasks of mathematical analysis and control design.

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The number of students with mental health challenges has increased significantly in public schools during the past 10 years. Despite school and community efforts many students remain underserved, specifically at-risk populations. At-risk youth are more likely to experience adverse life outcomes such as performing poorly in school, dropping out of school, acting violently, and facing higher instances of mental health challenges when compared to peers with fewer risk factors. The purpose of this qualitative study was to examine specialized instructional support personnel’s attitudes and willingness to implement a change model, ISF, to support at-risk youth in the public school system. A secondary aim of this study was to answer the question of the attitudes of specialized instructional support personnel to provide insight for decision makers considering adopting this change model. The research question posed was: What are specialized instructional support personnel’s attitudes toward implementing a mental health change model, ISF, in their school district? This study also contributed to furthering the knowledge of a complex service delivery system intended to enhance student performance by identifying support structures that promote student wellness. The results of this study indicated the specialized instructional support personnel interviewed would be willing to support elements of the ISF.