pwc-archive/papers-with-abstracts huggingface dataset:
Constraint Horizon in Model Predictive Control
Authors: Allan Andre do Nascimento, Han Wang, Antonis Papachristodoulou, Kostas Margellos
Date: 2025-03-24 00:00:00
Tasks: model, Model Predictive Control, Prediction

In this work, we propose a Model Predictive Control (MPC) formulation incorporating two distinct horizons: a prediction horizon and a constraint horizon. This approach enables a deeper understanding of how constraints influence key system properties such as suboptimality, without compromising recursive feasibility and constraint satisfaction. In this direction, our contributions are twofold. First, we provide a framework to estimate closed-loop optimality as a function of the number of enforced constraints. This is a generalization of existing results by considering partial constraint enforcement over the prediction horizon. Second, when adopting this general framework under the lens of safety-critical applications, our method improves conventional Control Barrier Function (CBF) based approaches. It mitigates myopic behaviour in Quadratic Programming (QP)-CBF schemes, and resolves compatibility issues between Control Lyapunov Function (CLF) and CBF constraints via the prediction horizon used in the optimization. We show the efficacy of the method via numerical simulations for a safety critical application.
Learning a Class of Mixed Linear Regressions: Global Convergence under General Data Conditions
Authors: Yujing Liu, Zhixin Liu, Lei Guo
Date: 2025-03-24 00:00:00
Tasks: Clustering

Mixed linear regression (MLR) has attracted increasing attention because of its great theoretical and practical importance in capturing nonlinear relationships by utilizing a mixture of linear regression sub-models. Although considerable efforts have been devoted to the learning problem of such systems, i.e., estimating data labels and identifying model parameters, most existing investigations employ the offline algorithm, impose the strict independent and identically distributed (i.i.d.) or persistent excitation (PE) conditions on the regressor data, and provide local convergence results only. In this paper, we investigate the recursive estimation and data clustering problems for a class of stochastic MLRs with two components. To address this inherently nonconvex optimization problem, we propose a novel two-step recursive identification algorithm to estimate the true parameters, where the direction vector and the scaling coefficient of the unknown parameters are estimated by the least squares and the expectation-maximization (EM) principles, respectively. Under a general data condition, which is much weaker than the traditional i.i.d. and PE conditions, we establish the global convergence and the convergence rate of the proposed identification algorithm for the first time. Furthermore, we prove that, without any excitation condition on the regressor data, the data clustering performance including the cumulative mis-classification error and the within-cluster error can be optimal asymptotically. Finally, we provide a numerical example to illustrate the performance of the proposed learning algorithm.
Dominant Groups and Asymmetric Polarization in Generalized Quasi-Structurally Balanced Networks
Authors: Vishnudatta Thota, Swati Priya, Twinkle Tripathy
Date: 2025-03-24 00:00:00

The paper focuses on the phenomenon of asymmetric polarization arising in the presence of a dominant group in the network. The existing works in the literature analyze polarization primarily in structurally and quasi-structurally balanced networks. In this work, we introduce generalized quasi-structurally balanced (GQSB) networks, which include both of these networks as special cases. In the presence of a dominant group, a GQSB network has a unique bipartition: the dominant group (and its allies) and the remaining agents. The dominant group's superior influence results in an asymmetry in how the inter-subset antagonistic interactions are perceived by both of the subsets. This, in turn, leads to asymmetry in the final polarized opinions. To model this behavior, we propose a generalized Laplacian flow for undirected GQSB networks with a dominant group and establish necessary and sufficient conditions for achieving asymmetric polarization. The theoretical results presented in this paper are validated through numerical simulations on the Highland Tribes real-world dataset.
Finite-Time Bounds for Two-Time-Scale Stochastic Approximation with Arbitrary Norm Contractions and Markovian Noise
Authors: Siddharth Chandak, Shaan ul Haque, Nicholas Bambos
Date: 2025-03-24 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning

Two-time-scale Stochastic Approximation (SA) is an iterative algorithm with applications in reinforcement learning and optimization. Prior finite time analysis of such algorithms has focused on fixed point iterations with mappings contractive under Euclidean norm. Motivated by applications in reinforcement learning, we give the first mean square bound on non linear two-time-scale SA where the iterations have arbitrary norm contractive mappings and Markovian noise. We show that the mean square error decays at a rate of $O(1/n^{2/3})$ in the general case, and at a rate of $O(1/n)$ in a special case where the slower timescale is noiseless. Our analysis uses the generalized Moreau envelope to handle the arbitrary norm contractions and solutions of Poisson equation to deal with the Markovian noise. By analyzing the SSP Q-Learning algorithm, we give the first $O(1/n)$ bound for an algorithm for asynchronous control of MDPs under the average reward criterion. We also obtain a rate of $O(1/n)$ for Q-Learning with Polyak-averaging and provide an algorithm for learning Generalized Nash Equilibrium (GNE) for strongly monotone games which converges at a rate of $O(1/n^{2/3})$.
Contact Plan Design for Cross-Linked GNSSs: An ILP Approach for Extended Applications
Authors: Huan Yan, Juan A. Fraire, Ziqi Yang, Kanglian Zhao, Wenfeng Li, Xiyun Hou, Haohan Li, Yuxuan Miao, Jinjun Zheng, Chengbin Kang, Huichao Zhou, Xinuo Chang, Lu Wang
Date: 2025-03-24 00:00:00

Global Navigation Satellite Systems (GNSS) employ inter-satellite links (ISLs) to reduce dependency on ground stations, enabling precise ranging and communication across satellites. Beyond their traditional role, ISLs can support extended applications, including providing navigation and communication services to external entities. However, designing effective contact plan design (CPD) schemes for these multifaceted ISLs, operating under a polling time-division duplex (PTDD) framework, remains a critical challenge. Existing CPD approaches focus solely on meeting GNSS satellites' internal ranging and communication demands, neglecting their extended applications. This paper introduces the first CPD scheme capable of supporting extended GNSS ISLs. By modeling GNSS requirements and designing a tailored service process, our approach ensures the allocation of essential resources for internal operations while accommodating external user demands. Based on the BeiDou constellation, simulation results demonstrate the proposed scheme's efficacy in maintaining core GNSS functionality while providing extended ISLs on a best-effort basis. Additionally, the results highlight the significant impact of GNSS ISLs in enhancing orbit determination and clock synchronization for the Earth-Moon libration point constellation, underscoring the importance of extended GNSS ISL applications.
MCE-based Direct FTC Method for Dynamic Positioning of Underwater Vehicles with Thruster Redundancy
Authors: Ji-Hong Li
Date: 2025-03-24 00:00:00
Tasks: parameter estimation

This paper presents an active model-based FTC (fault-tolerant control) method for the dynamic positioning of a class of underwater vehicles with thruster redundancy. Compared to the widely used state and parameter estimation methods, this proposed scheme directly utilizes the vehicle's motion control error (MCE) to construct a residual for detecting thruster faults and failures in the steady state of the control system. In the case of thruster fault identification, the most difficult aspect is that the actual control input with thruster faults is unknown. However, through a detailed and precise analyses of MCE variation trends in the case of thruster faults, highly useful information about this unknown control input can be extracted. This characteristic also serves as the foundation for the novel scheme proposed in this paper. As for control reconfiguration, it is straightforward since the thrust losses can be directly estimated as a result of the identification process. Numerical studies with the real world vehicle model are also carried out to demonstrate the effectiveness of the proposed method.
Optimized Contact Plan Design for Reflector and Phased Array Terminals in Cislunar Space Networks
Authors: Huan Yan, Juan A. Fraire, Ziqi Yang, Kanglian Zhao, Wenfeng Li, Yuan Fang, Jinjun Zheng, Chengbin Kang, Huichao Zhou, Xinuo Chang, Lu Wang, Linshan Xue
Date: 2025-03-24 00:00:00

Cislunar space is emerging as a critical domain for human exploration, requiring robust infrastructure to support spatial users - spacecraft with navigation and communication demands. Deploying satellites at Earth-Moon libration points offers an effective solution. This paper introduces a novel Contact Plan Design (CPD) scheme that considers two classes of cislunar transponders: Reflector Links (RL) for high-volume data transfer and Phased Array Links (PL) for fast switching and navigation services.Our approach addresses the needs of both satellites and spatial users within the Earth-Moon Libration Point Communication and Navigation Constellation (EMLP-CNC). Simulations validate the proposed scheme, demonstrating its effectiveness in serving spatial users while meeting satellite ranging and communication requirements. These findings provide essential insights for developing future Cislunar Space Infrastructures.
Transient synchronization stability analysis and assessment of DFIG system under severe faults
Authors: Hongsheng Xu, Meng Zhan
Date: 2025-03-24 00:00:00

In the transient stability analysis of renewable energy grid-tied systems, although a large amount of works have devoted to the detailed electromagnetic transient simulation and the stability analyses of during-fault stage, the whole low-voltage ride through (LVRT) process and relevant transient stability mechanism remain to be uncovered. Taking the doubly fed induction generator system as the objective, this paper divides the transient processes into four different stages, including the pre-fault, during-fault, early post-fault, and late post-fault ones, establishes the full mechanism models for each stage, and studies the switching dynamics in detail. It is found that the during-fault dynamics can be determined by the phase-lock loop second-order equation within the framework of the generalized swing equation (GSE). For the early post-fault stage, it can be treated as a series of quasi-steady states and its dominant driving system dynamics can still be described by the GSE. Based on the local dynamics of unstable equilibrium point, the system transient stability can be completely determined by whether the initial state of the early post-fault stage is within or out of its basin of attraction (BOA). Based on these observations, the BOA-based and equal area criterion (EAC)-based transient stability assessment methods are developed, which are supported by broad numerical simulations and hardware-in-the-loop experiments. This work provides a clear physical picture and perfectly solves the difficult stability analysis problem when severe faults and LVRT have to be considered in most of DFIG engineering situations.
Koopman-Nemytskii Operator: A Linear Representation of Nonlinear Controlled Systems
Authors: Wentao Tang
Date: 2025-03-24 00:00:00

While Koopman operator lifts a nonlinear system into an infinite-dimensional function space and represents it as a linear dynamics, its definition is restricted to autonomous systems, i.e., does not incorporate inputs or disturbances. To the end of designing state-feedback controllers, the existing extensions of Koopman operator, which only account for the effect of open-loop values of inputs, does not involve feedback laws on closed-loop systems. Hence, in order to generically represent any nonlinear controlled dynamics linearly, this paper proposes a Koopman-Nemytskii operator, defined as a linear mapping from a product reproducing kernel Hilbert space (RKHS) of states and feedback laws to an RKHS of states. Using the equivalence between RKHS and Sobolev-Hilbert spaces under certain regularity conditions on the dynamics and kernel selection, this operator is well-defined. Its data-based approximation, which follows a kernel extended dynamic mode decomposition (kernel EDMD) approach, have established errors in single-step and multi-step state predictions as well as accumulated cost under control.
An AI-enabled dual-hormone model predictive control algorithm that delivers insulin and pramlintide
Authors: Peter G. Jacobs, Wade Hilts, Robert Dodier, Joseph Leitschuh, Jae H. Eom, Deborah Branigan, Forrest Ling, Matthew Howard, Clara Mosquera-Lopez, Leah Wilson
Date: 2025-03-23 00:00:00
Tasks: Model Predictive Control

Current closed-loop insulin delivery algorithms need to be informed of carbohydrate intake disturbances. This can be a burden on people using these systems. Pramlintide is a hormone that delays gastric emptying, which enables insulin kinetics to align with the kinetics of carbohydrate absorption. Integrating pramlintide into an automated insulin delivery system can be helpful in reducing the postprandial glucose excursion and may be helpful in enabling fully-closed loop whereby meals do not need to be announced. We present an AI-enabled dual-hormone model predictive control (MPC) algorithm that delivers insulin and pramlintide without requiring meal announcements that uses a neural network to automatically detect and deliver meal insulin. The MPC algorithm includes a new pramlintide pharmacokinetics and pharmacodynamics model that was identified using data collected from people with type 1 diabetes undergoing a meal challenge. Using a simulator, we evaluated the performance of various pramlintide delivery methods and controller models, as well as the baseline insulin-only scenario. Meals were automatically dosed using a neural network meal detection and dosing (MDD) algorithm. The primary outcome was the percent time glucose is in the target range (TIR: 70-180 mg/dL). Results show that delivering pramlintide at a fixed ratio of 6 mcg pramlintide:1 u insulin using an MPC that is aware of the pramlintide achieved the most significant improved TIR compared with an insulin-only MPC (91.6% vs. 64.1%). Delivering pramlintide as a fixed ratio was better than delivering basal pramlintide at a constant rate, indicating the benefit of the MDD algorithm. There was no advantage of independent control of insulin and pramlintide compared with insulin and pramlintide delivered as a fixed ratio. Preliminary real-world results from a human subject further indicate the benefit of pramlintide delivery.
Probabilistic Net Load Forecasting for High-Penetration RES Grids Utilizing Enhanced Conditional Diffusion Model
Authors: Yixiang Huang, Jianhua Pei, Luocheng Chen, Zhenchang Du, Jinfu Chen, Zirui Peng
Date: 2025-03-22 00:00:00
Tasks: Density Estimation, Imputation, Load Forecasting, Uncertainty Quantification

The proliferation of intermittent distributed renewable energy sources (RES) in modern power systems has fundamentally compromised the reliability and accuracy of deterministic net load forecasting. Generative models, particularly diffusion models, demonstrate exceptional potential in uncertainty quantification for scenario forecasting. Nevertheless, their probabilistic predictive capabilities and conditional bootstrapping mechanisms still remain underexplored. In this paper, a day-ahead probabilistic net load forecasting framework is developed by systematically quantifying epistemic uncertainty and aleatoric variability using the feature-informed enhanced conditional diffusion model (ECDM). The ECDM architecture implements the net load distribution generation process using an imputation-based conditional diffusion model, where multi-modal conditional inputs, such as weather and calendar data, are fused via cross-attention mechanisms. Specifically, historical net load profiles are utilized to guide the reverse diffusion trajectory through non-parametric imputation operators preserving spatial-temporal integrity. To capture periodic characteristics, a novel weekly arrangement method is also introduced, while an unconditional model is integrated to ensure diversity in the generated scenarios. Subsequently, the maximum probabilistic points and probability intervals of predicted net load are obtained by the adaptive kernel density estimation under RES intermittency. Moreover, ECDM is extented to multi-energy forecast framework, attempting to increase interpretability of the net load predictions. Numerical experiments on a publicly available dataset demonstrate the superior forecasting performance of the proposed method compared to existing state-of-the-art approaches.
Enhancing Fault Detection in CO2 Refrigeration Systems: Optimal Sensor Selection and Robustness Analysis Using Tree-Based Machine Learning
Authors: Masoud Kishani Farahani, Morteza Kolivandi, Abbas Rajabi Ghahnavieh, Mohammad Talaei
Date: 2025-03-22 00:00:00
Tasks: Fault Detection

This study investigates the reliability and robustness of data-driven Fault Detection and Diagnosis (FDD) models for CO2 refrigeration systems (CO2-RS) in supermarkets, focusing on optimal sensor selection and resilience against sensor noise. Using tree-based machine learning algorithms - Random Forest (RF), XGBoost, CatBoost, and LightGBM - we developed FDD models to classify six common faults in a laboratory-scale CO2-RS. The Recursive Feature Addition (RFA) approach identified optimal sensor sets, achieving a 99% F1-score with minimal sensors: four for RF, seven for XGBoost, five for CatBoost, and five for LightGBM. Condenser-side sensors consistently ranked as critical for fault detection. Robustness was assessed by injecting Additive White Gaussian Noise (AWGN) at a signal-to-noise ratio (SNR) of 3 dB into the most important sensor, with XGBoost showing superior resilience at 85.24%, followed by CatBoost (57.07%), LightGBM (49.1%), and RF (49.46%). Sensitivity analysis across high-SNR (10 dB), low-SNR (0 dB), and sensor failure scenarios revealed XGBoost's robustness peaking at 90.23% and retaining 79% under failure, contrasting with sharper declines in other models. These findings highlight a trade-off between sensor count, cost, and reliability, with larger ensembles enhancing noise resilience. This work bridges gaps in FDD literature by integrating sensor optimization with comprehensive robustness analysis, offering a practical framework for improving energy efficiency and fault management in CO2-RS. Future efforts could explore adaptive SNR thresholds and redundant sensor configurations to enhance real-world applicability.
Mixed-gradients Distributed Filtered Reference Least Mean Square Algorithm -- A Robust Distributed Multichannel Active Noise Control Algorithm
Authors: Junwei Ji, Dongyuan Shi, Woon-Seng Gan
Date: 2025-03-22 00:00:00
Tasks: Computational Efficiency

Distributed multichannel active noise control (DMCANC), which utilizes multiple individual processors to achieve a global noise reduction performance comparable to conventional centralized multichannel active noise control (MCANC), has become increasingly attractive due to its high computational efficiency. However, the majority of current DMCANC algorithms disregard the impact of crosstalk across nodes and impose the assumption of an ideal network devoid of communication limitations, which is an unrealistic assumption. Therefore, this work presents a robust DMCANC algorithm that employs the compensating filter to mitigate the impact of crosstalk. The proposed solution enhances the DMCANC system's flexibility and security by utilizing local gradients instead of local control filters to convey enhanced information, resulting in a mixed-gradients distributed filtered reference least mean square (MGDFxLMS) algorithm. The performance investigation demonstrates that the proposed approach performs well with the centralized method. Furthermore, to address the issue of communication delay in the distributed network, a practical strategy that auto-shrinks the step size value in response to the delayed samples is implemented to improve the system's resilience. The numerical simulation results demonstrate the efficacy of the proposed auto-shrink step size MGDFxLMS (ASSS-MGDFxLMS) algorithm across various communication delays, highlighting its practical value.
A robust mechanical sensorless control strategy for active rectification of small wind turbines
Authors: Adrien Prévost, Vincent Léchappé, Romain Delpoux, Xavier Brun
Date: 2025-03-21 00:00:00

This article proposes a mechanical sensorless control strategy for the synchronous rectification of small wind turbines equipped with a surface-mounted Permanent Magnet Synchronous Generator (PMSG). By means of Lyapunov theory, the Global Asymptotic Stability (GAS) of the closed loop system is proven. It allows the use of a classical Sliding Mode Observer (SMO) to remove the mechanical sensor in the control loop despite uncertainties on the resistance and inductance parameters. The analysis of the equilibrium points have made it possible to propose an analytic model of the angular misalignment between the true and the observer rotating frames, responsible for current tracking errors. Experimental tests on a wind turbine emulator show that despite large errors on the the resistance and inductance parameters, the impact on the energy harvest is low, proving that the strategy's performance is robust to high uncertainties.
Electric Vehicle Integration using Large-Scale Combined Transmission and Distribution Grid Models
Authors: Diana Wallison, Lyric Haylow, Jessica Wert, Jonathan M. Snodgrass, Thomas J. Overbye, Yanzhi, Xu
Date: 2025-03-21 00:00:00

In this paper, we propose a unifying co-simulation framework integrating transportation demand, grid assets, land use, demographics, and emissions to optimally accelerate electric vehicle (EV) development as well as measure the impact of EV integration. 96 urban and long-haul truck charging demand simulations were developed and integrated into a combined transmission and distribution (T&D) simulation, encompassing the Houston/Dallas/Fort Worth area. The T&D scenarios are then used to develop cost optimization strategies to determine optimal placement and sizing of truck charging infrastructure that minimize infrastructure costs.
The Impedance Space: A Look at Mechanical Impedance Ellipses in 3D
Authors: Leonardo F. Dos Santos, Cícero Zanette, Elisa G. Vergamini, Lucca Maitan, Thiago Boaventura
Date: 2025-03-21 00:00:00

This paper introduces a novel 3D graphical representation of mechanical impedance, named impedance space, to enhance the analysis of the dynamic behavior of compliant systems. This approach addresses the limitations of existing 2D graphical methods by associating the impedance control parameters with linear transformations to plot a parametric 3D ellipse, for a known oscillatory input. The well-known 2D impedance ellipses are shown to be particular projections of this 3D ellipse. Experimental results demonstrate the effectiveness of the proposed representation.