pwc-archive/papers-with-abstracts huggingface dataset:
Abstracted Model Reduction: A General Framework for Efficient Interconnected System Reduction
Authors: Luuk Poort, Lars A. L. Janssen, Bart Besselink, Rob H. B. Fey, Nathan van de Wouw
Date: 2024-11-20 00:00:00

This paper introduces the concept of abstracted model reduction: a framework to improve the tractability of structure-preserving methods for the complexity reduction of interconnected system models. To effectively reduce high-order, interconnected models, it is usually not sufficient to consider the subsystems separately. Instead, structure-preserving reduction methods should be employed, which consider the interconnected dynamics to select which subsystem dynamics to retain in reduction. However, structure-preserving methods are often not computationally tractable. To overcome this issue, we propose to connect each subsystem model to a low-order abstraction of its environment to reduce it both effectively and efficiently. By means of a high-fidelity structural-dynamics model from the lithography industry, we show, on the one hand, significantly increased accuracy with respect to standard subsystem reduction and, on the other hand, similar accuracy to direct application of expensive structure-preserving methods, while significantly reducing computational cost. Furthermore, we formulate a systematic approach to automatically determine sufficient abstraction and reduction orders to preserve stability and guarantee a given frequency-dependent error specification. We apply this approach to the lithography equipment use case and show that the environment model can indeed be reduced by over 80\% without significant loss in the accuracy of the reduced interconnected model.
Analytic Design of Flat-Wire Inductors for High-Current and Compact DC-DC Converters
Authors: Sajjad Mohammadi, James L. Kirtley, Alireza Namadmalan
Date: 2024-11-20 00:00:00

This paper presents analytic study and design considerations of flat wire inductors with distributed gaps for high-power and compact DC-DC Converters. The focus is eddy current loss components within the conductors due to fringing and leakage fluxes. A magnetic equivalent circuit (MEC) is proposed in which eddy currents are modeled by MMFs opposing the primary flux as well as frequency dependent reluctances, which finally leads to a frequency dependent inductance describing the behavior of the inductor at high frequencies. Three formulations for DC resistance depending on the required accuracy are developed. Calculations of the AC resistance based on vector potential obtained from FEM are provided. To provide an insight into the optimized design of such inductors, components of the magnetic flux and induced eddy currents along with sensitivity of the main inductor quantities such as DCR, ESR, loss components and inductance values to the design parameters are investigated. Finally, an inductor is prototyped and experimentally tested to verify the design.
Unified Performance Control for Non-Square Nonlinear Systems with Relaxed Controllability
Authors: Bing Zhou, Kai Zhao, Yongduan Song
Date: 2024-11-20 00:00:00

In this paper, we investigate the problem of unified prescribed performance tracking for a class of non-square strict-feedback nonlinear systems in the presence of actuator faults under relaxed controllability conditions. By using a skillful matrix decomposition and introducing some feasible auxiliary matrices, a more generalized controllability condition than the current state of the art is constructed, which can be applied to both square and non-square nonlinear systems subject to actuator faults and unknown yet time-varying control gain. Incorporating the relaxed controllability conditions and the uniform performance specifications into the backstepping design procedure, a prescribed performance fault-tolerant controller is developed that can achieve different performance demands without modifying the controller structure, which is more flexible and practical. In addition, the destruction of the system stability by unknown auxiliary matrices and unknown nonlinearities is circumvented by embedding the available core information of the state-dependent uncertainties into the design procedure. Both theoretical analysis and numerical simulation demonstrate the effectiveness and benefits of the proposed method.
Identification of Black-Box Inverter-Based Resource Control Using Hammerstein-Wiener Models
Authors: Aldin Dželo, Amer Mešanović, Mirsad Cosovic
Date: 2024-11-20 00:00:00

The development of more complex inverter-based resources (IBRs) control is becoming essential as a result of the growing share of renewable energy sources in power systems. Given the diverse range of control schemes, grid operators are typically provided with black-box models of IBRs from various equipment manufacturers. As such, they are integrated into simulation models of the entire power system for analysis, and due to their nature, they can only be simulated in the time domain. Other system analysis approaches, like eigenvalue analysis, cannot be applied, making the comprehensive analysis of defined systems more challenging. This work introduces an approach for identification of three-phase IBR models for grid-forming and grid-following inverters using Hammerstein-Wiener models. To this end, we define a simulation framework for the identification process, and select suitable evaluation metrics for the results. Finally, we evaluate the approach on generic grid-forming and grid-following inverter models showing good identification results.
Quantitative Fairness -- A Framework For The Design Of Equitable Cybernetic Societies
Authors: Kevin Riehl, Michail Makridis, Anastasios Kouvelas
Date: 2024-11-20 00:00:00
Tasks: Decision Making, Fairness

Advancements in computer science, artificial intelligence, and control systems of the recent have catalyzed the emergence of cybernetic societies, where algorithms play a significant role in decision-making processes affecting the daily life of humans in almost every aspect. Algorithmic decision-making expands into almost every industry, government processes critical infrastructure, and shapes the life-reality of people and the very fabric of social interactions and communication. Besides the great potentials to improve efficiency and reduce corruption, missspecified cybernetic systems harbor the threat to create societal inequities, systematic discrimination, and dystopic, totalitarian societies. Fairness is a crucial component in the design of cybernetic systems, to promote cooperation between selfish individuals, to achieve better outcomes at the system level, to confront public resistance, to gain trust and acceptance for rules and institutions, to perforate self-reinforcing cycles of poverty through social mobility, to incentivize motivation, contribution and satisfaction of people through inclusion, to increase social-cohesion in groups, and ultimately to improve life quality. Quantitative descriptions of fairness are crucial to reflect equity into algorithms, but only few works in the fairness literature offer such measures; the existing quantitative measures in the literature are either too application-specific, suffer from undesirable characteristics, or are not ideology-agnostic. Therefore, this work proposes a quantitative, transactional, distributive fairness framework, which enables systematic design of socially feasible decision-making systems. Moreover, it emphasizes the importance of fairness and transparency when designing algorithms for equitable, cybernetic societies.
Robust Convergency Indicator using High-dimension PID Controller in the presence of disturbance
Authors: Sheng Zimao, Yang Hongan, Wang Jiakang, Song Peng, Zhang Tong
Date: 2024-11-20 00:00:00

The PID controller currently occupies a prominent position as the most prevalent control architecture, which has achieved groundbreaking success across extensive implications. However, its parameters online regulation remains a formidable challenge. The majority of existing theories hinge on the linear constant system structure, contemplating only Single-Input, Single-Output (SISO) scenarios. Restricted research has been conducted on the intricate PID control problem within high-dimensional, Multi-Input, Multi-Output (MIMO) nonlinear systems that incorporate disturbances. This research, providing insights on the velocity form of nonlinear system, aims to bolster the controller's robustness. It establishes a quantitative metric to assess the robustness of high-dimensional PID controller, elucidates the pivotal theory regarding robustness's impact on error exponential convergence, and introduces a localized compensation strategy to optimize the robustness indicator. Guided by these theoretical insights, we exploit a robust high-dimensional PID (RH-PID) controller without the crutch of oversimplifying assumptions. Experimental results demonstrate the controller's commendable exponential stabilization efficacy and the controller exhibits exceptional robustness under the robust indicator's guidance. Notably, the robust convergence indicator can also effectively evaluate the comprehensive performance.
From Signal Space To STP-CS
Authors: Daizhan Cheng
Date: 2024-11-20 00:00:00
Tasks: compressed sensing

Under the assumption that a finite signal with different sampling lengths or different sampling frequencies is considered as equivalent, the signal space is considered as the quotient space of $\mathbb{R}^{\infty}$ over equivalence. The topological structure and the properties of signal space are investigated. Using them some characteristics of semi-tensor product based compressed sensing (STP-CS) are revealed. Finally, a systematic analysis of the construction of sensing matrix based on balanced incomplete block design (BIBD) is presented.
Probabilistic Dynamic Line Rating Forecasting with Line Graph Convolutional LSTM
Authors: Minsoo Kim, Vladimir Dvorkin, Jip Kim
Date: 2024-11-20 00:00:00
Tasks: Scheduling

Dynamic line rating (DLR) is a promising solution to increase the utilization of transmission lines by adjusting ratings based on real-time weather conditions. Accurate DLR forecast at the scheduling stage is thus necessary for system operators to proactively optimize power flows, manage congestion, and reduce the cost of grid operations. However, the DLR forecast remains challenging due to weather uncertainty. To reliably predict DLRs, we propose a new probabilistic forecasting model based on line graph convolutional LSTM. Like standard LSTM networks, our model accounts for temporal correlations between DLRs across the planning horizon. The line graph-structured network additionally allows us to leverage the spatial correlations of DLR features across the grid to improve the quality of predictions. Simulation results on the synthetic Texas 123-bus system demonstrate that the proposed model significantly outperforms the baseline probabilistic DLR forecasting models regarding reliability and sharpness while using the fewest parameters.
Matrix-Scheduling of QSR-Dissipative Systems
Authors: Sepehr Moalemi, James Richard Forbes
Date: 2024-11-20 00:00:00
Tasks: Scheduling

This paper considers gain-scheduling of QSR-dissipative subsystems using scheduling matrices. The corresponding QSR-dissipative properties of the overall matrix-gain-scheduled system, which depends on the QSR properties of the subsystems scheduled, are explicitly derived. The use of scheduling matrices is a generalization of the scalar scheduling signals used in the literature, and allows for greater design freedom when scheduling systems, such as in the case of gain-scheduled control. Furthermore, this work extends the existing gain-scheduling results to a broader class of QSR-dissipative systems. The matrix-scheduling of important special cases, such as passive, input strictly passive, output strictly passive, finite L2 gain, very strictly passive, and conic systems are presented. The proposed gain-scheduling architecture is used in the context of controlling a planar three-link robot subject to model uncertainty. A novel control synthesis technique is used to design QSR-dissipative subcontrollers that are gain-scheduled using scheduling matrices. Numerical simulation results highlight the greater design freedom of scheduling matrices, leading to improved performance.
Stabilization of Switched Affine Systems With Dwell-Time Constraint
Authors: Antonio Russo, Gian Paolo Incremona, Patrizio Colaneri
Date: 2024-11-20 00:00:00

This paper addresses the problem of stabilization of switched affine systems under dwell-time constraint, giving guarantees on the bound of the quadratic cost associated with the proposed state switching control law. Specifically, two switching rules are presented relying on the solution of differential Lyapunov inequalities and Lyapunov-Metzler inequalities, from which the stability conditions are expressed. The first one allows to regulate the state of linear switched systems to zero, whereas the second one is designed for switched affine systems proving practical stability of the origin. In both cases, the determination of a guaranteed cost associated with each control strategy is shown. In the cases of linear and affine systems, the existence of the solution for the Lyapunov-Metzler condition is discussed and guidelines for the selection of a solution ensuring suitable performance of the system evolution are provided. The theoretical results are finally assessed by means of three examples.
Experimental Study of Underwater Acoustic Reconfigurable Intelligent Surfaces with In-Phase and Quadrature Modulation
Authors: Yu Luo, Lina Pu, Aijun Song
Date: 2024-11-19 00:00:00

This paper presents an underwater acoustic reconfigurable intelligent surfaces (UA-RIS) designed for long-range, high-speed, and environmentally friendly communication in oceanic environments. The proposed UA-RIS comprises multiple pairs of acoustic reflectors that utilize in-phase and quadrature (IQ) modulation to flexibly control the amplitude and phase of reflected waves. This capability enables precise beam steering to enhance or attenuate sound levels in specific directions. A prototype UA-RIS with 4*6 acoustic reflection units is constructed and tested in both tank and lake environments to evaluate performance. The experimental results indicate that the prototype is capable of effectively pointing reflected waves to targeted directions while minimizing side lobes using passive IQ modulation. Field tests reveal that deploying the UA-RIS on the sender side considerably extends communication ranges by 28% in deep water and 46% in shallow waters. Furthermore, with a fixed communication distance, positioning the UA-RIS at the transmitter side substantially boosts data rates, with an average increase of 63.8% and peaks up to 96%. When positioned on the receiver side, the UA-RIS can expand the communication range in shallow and deep water environments by 40.6% and 66%, respectively. Moreover, placing the UA-RIS close to the receiver enhances data rates by an average of 80.3%, reaching up to 163% under certain circumstances.
Adaptive Control Barrier Functions with Vanishing Conservativeness Under Persistency of Excitation
Authors: Ricardo Gutierrez, Jesse B. Hoagg
Date: 2024-11-19 00:00:00

This article presents a closed-form adaptive controlbarrier-function (CBF) approach for satisfying state constraints in systems with parametric uncertainty. This approach uses a sampled-data recursive-least-squares algorithm to estimate the unknown model parameters and construct a nonincreasing upper bound on the norm of the estimation error. Together, this estimate and upper bound are used to construct a CBF-based constraint that has nonincreasing conservativeness. Furthermore, if a persistency of excitation condition is satisfied, then the CBFbased constraint has vanishing conservativeness in the sense that the CBF-based constraint converges to the ideal constraint corresponding to the case where the uncertainty is known. In addition, the approach incorporates a monotonically improving estimate of the unknown model parameters thus, this estimate can be effectively incorporated into a desired control law. We demonstrate constraint satisfaction and performance using 2 two numerical examples, namely, a nonlinear pendulum and a nonholonomic robot.
Omnidirectional Wireless Power Transfer for Millimetric Magnetoelectric Biomedical Implants
Authors: Wei Wang, Zhanghao Yu, Yiwei Zou, Joshua E Woods, Prahalad Chari, Yumin Su, Jacob T Robinson, Kaiyuan Yang
Date: 2024-11-19 00:00:00

Miniature bioelectronic implants promise revolutionary therapies for cardiovascular and neurological disorders. Wireless power transfer (WPT) is a significant method for miniaturization, eliminating the need for bulky batteries in devices. Despite successful demonstrations of millimetric battery free implants in animal models, the robustness and efficiency of WPT are known to degrade significantly under misalignment incurred by body movements, respiration, heart beating, and limited control of implant orientation during surgery. This article presents an omnidirectional WPT platform for millimetric bioelectronic implants, employing the emerging magnetoelectric (ME) WPT modality, and magnetic field steering technique based on multiple transmitter (TX) coils. To accurately sense the weak coupling in a miniature implant and adaptively control the multicoil TX array in a closed loop, we develop an active echo (AE) scheme using a tiny coil on the implant. Our prototype comprises a fully integrated 14.2 mm3 implantable stimulator embedding a custom low power system on chip (SoC) powered by an ME film, a TX with a custom three channel AE RX chip, and a multicoil TX array with mutual inductance cancellation. The AE RX achieves negative 161 dBm per Hz input referred noise with 64 dB gain tuning range to reliably sense the AE signal, and offers fast polarity detection for driver control. AE simultaneously enhances the robustness, efficiency, and charging range of ME WPT. Under 90 degree rotation from the ideal position, our omnidirectional WPT system achieves 6.8x higher power transfer efficiency (PTE) than a single coil baseline. The tracking error of AE negligibly degrades the PTE by less than 2 percent from using ideal control.
DIETS: Diabetic Insulin Management System in Everyday Life
Authors: Hanyu Zeng, Hui Ji, Pengfei Zhou
Date: 2024-11-19 00:00:00
Tasks: Large Language Model, Management

People with diabetes need insulin delivery to effectively manage their blood glucose levels, especially after meals, because their bodies either do not produce enough insulin or cannot fully utilize it. Accurate insulin delivery starts with estimating the nutrients in meals and is followed by developing a detailed, personalized insulin injection strategy. These tasks are particularly challenging in daily life, especially without professional guidance. Existing solutions usually assume the prior knowledge of nutrients in meals and primarily rely on feedback from professional clinicians or simulators to develop Reinforcement Learning-based models for insulin management, leading to extensive consumption of medical resources and difficulties in adapting the models to new patients due to individual differences. In this paper, we propose DIETS, a novel diabetic insulin management framework built on the transformer architecture, to help people with diabetes effectively manage insulin delivery in everyday life. Specifically, DIETS tailors a Large Language Model (LLM) to estimate the nutrients in meals and employs a titration model to generate recommended insulin injection strategies, which are further validated by a glucose prediction model to prevent potential risks of hyperglycemia or hypoglycemia. DIETS has been extensively evaluated on three public datasets, and the results show it achieves superior performance in providing effective insulin delivery recommendation to control blood glucose levels.
Distributed Coordination of Grid-Forming and Grid-Following Inverters for Optimal Frequency Control in Power Systems
Authors: Xiaoyang Wang, Xin Chen
Date: 2024-11-19 00:00:00

The large-scale integration of inverter-interfaced renewable energy sources presents significant challenges to maintaining power balance and nominal frequency in modern power systems. This paper studies grid-level coordinated control of grid-forming (GFM) and grid-following (GFL) inverter-based resources (IBRs) for scalable and optimal frequency control. We propose a fully distributed optimal frequency control algorithm based on the projected primal-dual gradient method and by leveraging the structure of the underlying physical system dynamics. The proposed algorithm i) restores the nominal system frequency while minimizing total control cost and enforcing IBR power capacity limits and line thermal constraints, and ii) operates in a distributed manner that only needs local measurements and neighbor-to-neighbor communication. In particular, when the line thermal constraints are disregarded, the proposed algorithm admits a fully local implementation that requires no communication, while still ensuring optimality and satisfying IBR power capacity limits. We establish the global asymptotic convergence of the algorithm using Lyapunov stability analysis. The effectiveness and optimality of the proposed algorithms are validated through high-fidelity, 100% inverter-based electromagnetic transient (EMT) simulations on the IEEE 39-bus system.
Steady-State Initialization of Object-Oriented Advanced Thermal Power Generation System Models with Application to the Case of the SOS-CO2 Cycle
Authors: Matteo Luigi De Pascali, Francesco Casella
Date: 2024-11-19 00:00:00

The forthcoming energy transition calls for a new generation of thermal power generation systems with low- or zero-emission and highly flexible operation. Dynamic modelling and simulation is a key enabling factor in this field, as controlling such plants is a difficult task for which there is no previous experience and very short design times are expected. The steady-state initialization of those dynamic models is an essential step in the design process, but is unfortunately a difficult task which involves the numerical solution of large systems of nonlinear equations with iterative Newton methods, which is often prone to numerical failures. In this work, several strategies and methodologies are discussed to successfully achieve steady-state initialization of first-principles equation-based, object-oriented models of advanced thermal power generation systems. These are presented in the context of the Modelica modelling language, but could be applied to other equation-based, object-oriented modelling and simulation environments. Finally, the successful application of such strategies and methodologies to the SOS-CO2 advanced power generation system is presented.