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1. Decentralized Control of Constrained Linear Systems via Assume-Guarantee Contracts

   https://doi.org/10.23919/ACC45564.2020.9147215  

   Lin, Weixuan; Bitar, Eilyan (July 2020, 2020 American Control Conference (ACC))

   null (Ed.)

   We consider the decentralized control of a discretetime, linear system subject to exogenous disturbances and polyhedral constraints on the state and input trajectories. The underlying system is composed of a finite collection of dynamically coupled subsystems, where each subsystem is assumed to have a dedicated local controller. The decentralization of information is expressed according to sparsity constraints on the state measurements that each local controller has access to. In this context, we investigate the design of decentralized controllers that are affinely parameterized in their measurement history. For problems with partially nested information structures, the optimization over such policy spaces is known to be convex. Convexity is not, however, guaranteed under more general (nonclassical) information structures in which the information available to one local controller can be affected by control actions that it cannot access or reconstruct. With the aim of alleviating the nonconvexity that arises in such problems, we propose an approach to decentralized control design where the information-coupling states are effectively treated as disturbances whose trajectories are constrained to take values in ellipsoidal contract sets whose location, scale, and orientation are jointly optimized with the underlying affine decentralized control policy. We establish a natural structural condition on the space of allowable contracts that facilitates the joint optimization over the control policy and the contract set via semidefinite programming. 

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2. Graph states of atomic ensembles engineered by photon-mediated entanglement

   https://doi.org/10.1038/s41567-024-02407-1  

   Cooper, Eric_S; Kunkel, Philipp; Periwal, Avikar; Schleier-Smith, Monika (March 2024, Nature Physics)

   Abstract Graph states are a broad family of entangled quantum states, each defined by a graph composed of edges representing the correlations between subsystems. Such states constitute versatile resources for quantum computation and quantum-enhanced measurement. Their generation and engineering require a high level of control over entanglement. Here we report on the generation of continuous-variable graph states of atomic spin ensembles, which form the nodes of the graph. We program the entanglement structure encoded in the graph edges by combining global photon-mediated interactions in an optical cavity with local spin rotations. By tuning the entanglement between two subsystems, we either localize correlations within each subsystem or enable Einstein–Podolsky–Rosen steering—a strong form of entanglement that enables the extraction of precise information from one subsystem through measurements on the other. We further engineer a four-mode square graph state, highlighting the flexibility of our approach. Our method is scalable to larger and more complex graphs, laying groundwork for measurement-based quantum computation and advanced protocols in quantum metrology. 

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3. Simultaneous Detection and Estimation of False Data Injection Attacks in Cyber-Physical Battery Systems using a Learning Observer

   https://doi.org/10.1109/ICCAD57653.2023.10152377  

   Chen, Wen; Lin, Feng; Wang, Le Yi (May 2023, IEEE)

   This work is to present a learning observer-based method for simultaneous detection and estimation of false data injection attacks (FDIAs) to the cyber-physical battery systems. The original battery system in a state-space formulation is transformed into two separate subsystems: one contains both disturbances and the FDIAs and the second one is free from disturbances but subject to FDIAs. A learning observer is then designed for the second subsystem such that the FDIA signals can be estimated and further detected without being affected by the disturbances. This makes the proposed learning observer-based detection and estimation method is robust to disturbances and false declaration of FDIAs can be avoided. Another advantage of the proposed method is that the computing load is low because of the design of a reduced-order learning observer. With a three-cell battery string, a simulation study is employed to verify the effectiveness of proposed detection and estimation method for the FDIAs. 

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4. Distributed Learning-based Stability Assessment for Large Scale Networks of Dissipative Systems

   https://doi.org/10.1109/CDC45484.2021.9683774  

   Jena, Amit; Huang, Tong; Sivaranjani, S.; Kalathil, Dileep; Xie, Le (December 2021, IEEE Conference on Decision and Control (CDC))

   We propose a new distributed learning-based framework for stability assessment of a class of networked nonlinear systems, where each subsystem is dissipative. The aim is to learn, in a distributed manner, a Lyapunov function and associated region of attraction for the networked system. We begin by using a neural network function approximation to learn a storage function for each subsystem such that the subsystem satisfies a local dissipativity property. We next use a satisfiability modulo theories (SMT) solver based falsifier that verifies the local dissipativity of each subsystem by deter- mining an absence of counterexamples that violate the local dissipativity property, as established by the neural network approximation. Finally, we verify network-level stability by using an alternating direction method of multipliers (ADMM) approach to update the storage function of each subsystem in a distributed manner until a global stability condition for the network of dissipative subsystems is satisfied. This step also leads to a network-level Lyapunov function that we then use to estimate a region of attraction. We illustrate the proposed algorithm and its advantages on a microgrid interconnection with power electronics interfaces. 

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5. A Principal-Agent Model of Systems Engineering Processes with Application to Satellite Design

   Safarkhani, Salar; Kattakuri, Vikranth; Bilionis, I.; Panchal, Jitesh H. (July 2018, Council of Engineering Systems Universities (CESUN) Global Conference)

   We present a principal-agent model of a one-shot, shallow, systems engineering process. The process is "one-shot" in the sense that decisions are made during a one-time step and that they are final. The term "shallow" refers to a one-layer hierarchy of the process. Specifically, we assume that the systems engineer has already decomposed the problem in subsystems and that each subsystem is assigned to a different subsystem engineer. Each subsystem engineer works independently to maximize their own expected payoff. The goal of the systems engineer is to maximize the system-level payoff by incentivizing the subsystem engineers. We restrict our attention to requirements-based system-level payoffs, i.e., the systems engineer makes a profit only if all the design requirements are met. We illustrate the model using the design of an Earth-orbiting satellite system where the systems engineer determines the optimum incentive structures and requirements for two subsystems: the propulsion subsystem and the power subsystem. The model enables the analysis of a systems engineer's decisions about optimal passed-down requirements and incentives for sub-system engineers under different levels of task difficulty and associated costs. Sample results, for the case of risk-neutral systems and subsystems engineers, show that it is not always in the best interest of the systems engineer to pass down the true requirements. As expected, the model predicts that for small to moderate task uncertainties the optimal requirements are higher than the true ones, effectively eliminating the probability of failure for the systems engineer. In contrast, the model predicts that for large task uncertainties the optimal requirements should be smaller than the true ones in order to lure the subsystem engineers into participation. 

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