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1. Toward a Theory of Systems Engineering Processes: A Principal–Agent Model of a One-Shot, Shallow Process

   https://doi.org/10.1109/JSYST.2020.2964668  

   Safarkhani, Salar; Bilionis, Ilias; Panchal, Jitesh H. (January 2020, IEEE Systems Journal)

   Systems engineering processes (SEPs) coordinate the effort of different individuals to generate a product satisfying certain requirements. As the involved engineers are self-interested agents, the goals at different levels of the systems engineering hierarchy may deviate from the system-level goals, which may cause budget and schedule overruns. Therefore, there is a need of a systems engineering theory that accounts for the human behavior in systems design. As experience in the physical sciences shows, a lot of knowledge can be generated by studying simple hypothetical scenarios, which nevertheless retain some aspects of the original problem. To this end, the objective of this article is to study the simplest conceivable SEP, a principalagent model of a one-shot, shallow SEP. We assume that the systems engineer (SE) maximizes the expected utility of the system, while the subsystem engineers (sSE) seek to maximize their expected utilities. Furthermore, the SE is unable to monitor the effort of the sSE and may not have complete information about their types. However, the SE can incentivize the sSE by proposing specific contracts. To obtain an optimal incentive, we pose and solve numerically a bilevel optimization problem. Through extensive simulations, we study the optimal incentives arising from different system-level value functions under various combinations of effort costs, problem-solving skills, and task complexities. Our numerical examples show that, the passed-down requirements to the agents increase as the task complexity and uncertainty grow and they decrease with increasing the agents' costs. 

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2. Safe Control of Euler-Lagrange Systems with Limited Model Information

   https://doi.org/10.1109/CDC49753.2023.10384132  

   Wang, Yujie; Xu, Xiangru (December 2023, 62nd IEEE Conference on Decision and Control (CDC))

   This work presents a new safe control framework for Euler-Lagrange (EL) systems with limited model information, external disturbances, and measurement uncertainties. The EL system is decomposed into two subsystems called the proxy subsystem and the virtual tracking subsystem. An adaptive safe controller based on barrier Lyapunov functions is designed for the virtual tracking subsystem to ensure the boundedness of the safe velocity tracking error, and a safe controller based on control barrier functions is designed for the proxy subsystem to ensure controlled invariance of the safe set defined either in the joint space or task space. Theorems that guarantee the safety of the proposed controllers are provided. In contrast to existing safe control strategies for EL systems, the proposed method requires much less model information and can ensure safety rather than input-to-state safety. Simulation results are provided to illustrate the effectiveness of the proposed method. 

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3. Distributed and Asynchronous Coordination of a Mixed-Integer Linear System via Surrogate Lagrangian Relaxation

   https://doi.org/10.1109/TASE.2020.2998048  

   Bragin, Mikhail A.; Yan, Bing; Luh, Peter B. (January 2020, IEEE Transactions on Automation Science and Engineering)

   With the emergence of the Internet of Things that allows communications and local computations and with the vision of Industry 4.0, a foreseeable transition is from centralized system planning and operation toward decentralization with interacting components and subsystems, e.g., self-optimizing factories. In this article, a new ``price-based'' decomposition and coordination methodology is developed to efficiently coordinate a system consisting of distributed subsystems such as machines and parts, which are described by mixed-integer linear programming (MILP) formulations, in an asynchronous way. The novel method is a dual approach, whereby the coordination is performed by updating Lagrangian multipliers based on economic principles of ``supply and demand.'' To ensure low communication requirements within the method, exchanges between the ``coordinator'' and subsystems are limited to ``prices'' (Lagrangian multipliers) broadcast by the coordinator and to subsystem solutions sent at the coordinator. Asynchronous coordination, however, may lead to convergence difficulties since the order in which subsystem solutions arrive at the coordinator is not predefined as a result of uncertainties in communication and solving times. Under realistic assumptions of finite communication and solve times, the convergence of our method is proven by innovatively extending the Lyapunov stability theory. Numerical testing of generalized assignment problems through simulation demonstrates that the method converges fast and provides near-optimal results, paving the way for self-optimizing factories in the future. Accompanying CPLEX codes and data are included. 

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4. The Cost on System Performance of Requirements on Differentiable Variables

   https://doi.org/10.1115/1.4049336  

   Hazelrigg, George A.; Stolfi, Philip (May 2021, Journal of Mechanical Design)

   Abstract System design is commonly thought of as a process of maximizing a design objective subject to constraints, among which are the system requirements. Given system-level requirements, a convenient management approach is to disaggregate the system into subsystems and to “flowdown” the system-level requirements to the subsystem or lower levels. We note, however, that requirements truly are constraints, and they typically impose a penalty on system performance. Furthermore, disaggregation of the system-level requirements into the flowdown requirements creates added sets of constraints, all of which have the potential to impose further penalties on overall system performance. This is a highly undesirable effect of an otherwise beneficial system design management process. This article derives conditions that may be imposed on the flowdown requirements to assure that they do not penalize overall system performance beyond the system-level requirement. 

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5. Global system errors to simultaneously improve the identification of subsystems with mixed data Gaussian process regression

   https://doi.org/10.1088/2632-2153/ad4e05  

   LaMack, Cameron_J; Schearer, Eric_M (May 2024, Machine Learning: Science and Technology)

   Abstract This paper explores the use of Gaussian process regression for system identification in control engineering. It introduces two novel approaches that utilize the data from a measured global system error. The paper demonstrates these approaches by identifying a simulated system with three subsystems, a one degree of freedom mass with two antagonist muscles. The first approach uses this whole-system error data alone, achieving accuracy on the same order of magnitude as subsystem-specific data ( $9.28\pm 0.87\text{N\hspace{0.17em}}\text{vs.\hspace{0.17em}}6.96\pm 0.32\text{N}$of total model errors). This is significant, as it shows that the same data set can be used to identify unique subsystems, as opposed to requiring a set of data descriptive of only a single subsystem. The second approach demonstrated in this paper mixes traditional subsystem-specific data with the whole system error data, achieving up to 98.71% model improvement. 

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