pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Observability and Performance Tradeoffs in Optimal Control
Authors: Filippos Fotiadis, Ufuk Topcu
Date: 2025-06-24 00:00:00

We develop a feedback controller that minimizes the observability of a set of adversarial sensors of a linear system, while adhering to strict closed-loop performance constraints. We quantify the effectiveness of adversarial sensors using the trace of their observability Gramian and its inverse, capturing both average observability and the least observable state directions of the system. We derive theoretical lower bounds on these metrics under performance constraints, characterizing the fundamental limits of observability reduction as a function of the performance tradeoff. Finally, we show that the performance-constrained optimization of the Gramian's trace can be formulated as a one-shot semidefinite program, while we address the optimization of its inverse through sequential semidefinite programming. Simulations on an aircraft show how the proposed scheme yields controllers that deteriorate adversarial observability while having near-optimal closed-loop performance.
PID Tuning via Desired Step Response Curve Fitting
Authors: Senol Gulgonul
Date: 2025-06-21 00:00:00

This paper presents a PID tuning method based on step response curve fitting (PID-SRCF) that utilizes L2-norm minimization for precise reference tracking and explicit transient response shaping. The algorithm optimizes controller parameters by minimizing the root-mean-square error between desired and actual step responses. The proposed approach determines optimal PID parameters by matching any closed-loop response to a desired system step response. Practically a first-order plus time delay model or a second-order system with defined settling time and overshoot requirements are preferred. The method has open-source implementation using constrained nonlinear optimization in MATLAB. Comparative evaluations demonstrate that PID-SRCF can replace known analytical methods like Ziegler Nichols, Lambda Tuning, Pole Placement, Dominant Pole and MATLAB proprietary PID tuning applications.
MDR-DeePC: Model-Inspired Distributionally Robust Data-Enabled Predictive Control
Authors: Shihao Li, Jiachen Li, Christopher Martin, Soovadeep Bakshi, Dongmei Chen
Date: 2025-06-24 00:00:00
Tasks: Benchmarking

This paper presents a Model-Inspired Distributionally Robust Data-enabled Predictive Control (MDR-DeePC) framework for systems with partially known and uncertain dynamics. The proposed method integrates model-based equality constraints for known dynamics with a Hankel matrix-based representation of unknown dynamics. A distributionally robust optimization problem is formulated to account for parametric uncertainty and stochastic disturbances. Simulation results on a triple-mass-spring-damper system demonstrate improved disturbance rejection, reduced output oscillations, and lower control cost compared to standard DeePC. The results validate the robustness and effectiveness of MDR-DeePC, with potential for real-time implementation pending further benchmarking.
Decision-Focused Learning for Neural Network-Constrained Optimization: Application to HVAC Management System
Authors: Pietro Favaro, Jean-François Toubeau, François Vallée, Yury Dvorkin
Date: 2025-06-24 00:00:00
Tasks: Management

Heating, Ventilation, and Air Conditioning (HVAC) is a major electricity end-use with a substantial potential for grid services such as demand response. Harnessing this flexibility requires accurate modeling of the thermal dynamics of buildings, which is challenging due to their nonlinear and repetitive behavior (e.g., daily pattern), which reduce the value of historical data. To address this issue, this paper presents an HVAC management system formulated as a Mixed Integer Quadratic Program (MIQP), where Neural Network (NN) models of thermal dynamics are embedded as exact mixed-integer linear constraints. We employ Decision-Focused Learning (DFL) which tunes the NN parameters to improve the HVAC performance rather than prediction metrics. However, the discrete nature of the MIQP poses challenges for this approach, as it leads to gradients that are undefined or discontinuous, thus impeding standard gradient-based training. Here, we employ Stochastic Smoothing (SS), which enables efficient gradient computation without the need to differentiate through the MIQP. Experiments on a realistic five-zone building using a high-fidelity building simulator demonstrate that the proposed SS-DFL approach outperforms conventional two-stage and relaxed DFL methods in both cost savings and grid service performance, highlighting its potential for scalable, grid-interactive building control.
Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control
Authors: Ján Boldocký, Shahriar Dadras Javan, Martin Gulan, Martin Mönnigmann, Ján Drgoňa
Date: 2025-06-24 00:00:00
Tasks: Model Predictive Control, Self-Supervised Learning

We propose a novel approach to solving input- and state-constrained parametric mixed-integer optimal control problems using Differentiable Predictive Control (DPC). Our approach follows the differentiable programming paradigm by learning an explicit neural policy that maps control parameters to integer- and continuous-valued decision variables. This policy is optimized via stochastic gradient descent by differentiating the quadratic model predictive control objective through the closed-loop finite-horizon response of the system dynamics. To handle integrality constraints, we incorporate three differentiable rounding strategies. The approach is evaluated on a conceptual thermal energy system, comparing its performance with the optimal solution for different lengths of the prediction horizon. The simulation results indicate that our self-supervised learning approach can achieve near-optimal control performance while significantly reducing inference time by avoiding online optimization, thus implying its potential for embedded deployment even on edge devices.
Resilience assessment framework for cyber-physical distribution power system based on coordinated cyber-physical attacks under dynamic game
Authors: Yulin Liu, Zhaojun Ruan, Libao Shi
Date: 2025-06-24 00:00:00

Owing to the advanced communication networks and intelligent electronic devices, the cyber-physical distribution systems (CPDSs) possess the capability to perform flexible economic dispatch and achieve rapid self-healing from extreme events. Meanwhile, the deep integration of cyber and physical systems makes CPDS vulnerable to coordinated cyber-physical attacks. In this paper, a resilience assessment framework for the CPDS under coordinated cyber-physical attacks is proposed to investigate the impact of the coordinated attacks on load loss and service restoration in CPDS. First, a three-stage defender-attacker-defender dynamic game model considering fake base station (FBS) and physical attacks for CPDS is established, aiming at seeking the optimal defense resource deployment strategy to enhance the resilience of the CPDS. The physical attack is launched to cause faults on the power lines, and the FBS attack is employed to interrupt the service of wireless cellular network to hinder the self-healing process of the CPDS. The lognormal shadowing model and search theory are applied to quantitatively describe the process of the coordinated cyber-physical attacks. Further, the constructed three-stage dynamic game model is equivalently recast as a tri-level max-min-max optimization model, which is solved using column-and-constraint generation combined with enumeration method. Finally, the effectiveness of the proposed resilience assessment framework and solution strategy is demonstrated by conducting simulation analysis on the modified IEEE 33-node CPDS and a real-world 47-node CPDS in China.
Implementing blind navigation through multi-modal sensing and gait guidance
Authors: Feifan Yan, Tianle Zeng, Meixi He
Date: 2025-06-24 00:00:00

By the year 2023, the global population of individuals with impaired vision has surpassed 220 million. People with impaired vision will find it difficult while finding path or avoiding obstacles, and must ask for auxiliary tools for help. Although traditional aids such as guide canes and guide dogs exist, they still have some shortcomings. In this paper, we present our wearable blind guiding device, what perform navigation guidance through our proposed Gait-based Guiding System. Our device innovatively integrates gait phase analysis for walking guide, and in terms of environmental perception, we use multimodal sensing to acquire diverse environment information. During the experiment, we conducted both indoor and outdoor experiments, and compared with the standard guide cane. The result shows superior performance of our device in blind guidance.
Implementation and Analysis of Different Geomagnetic Field Models for Attitude Determination and Control System (ADCS) of a Satellite
Authors: Hoor Bano, Tatiana Podladchikova, Bisma Sajid, Dmitry Ris
Date: 2025-06-24 00:00:00

An Attitude Determination and Control System is essential for orientation stability and performance of slew maneuvers on the satellite. This research focuses on comparing two different geomagnetic field models, Direct Dipole Model and International Geomagnetic Reference Field Model, for modeling of magnetometer and magnetorquers. Both these magnetic field models are compared and analyzed for two satellite attitude cases: orientation stability and unloading of reaction wheels. Magnetometer modeling is utilized to get sensor data for attitude determination and control to attain orientation stability. Whereas, the magnetorquer model aids in reaction wheel unloading, by performing the required actuation on the satellite, upon interaction with the Earth's magnetic field. The study offers a comprehensive lookout on the impact of geomagnetic field models on the overall ADCS performance, incorporating both attitude estimation and control via the sensor and actuator modeling. Apart from this, valuable insights are gained into selecting optimal models based on specific mission requirements and available computational resources. Finally, this comparison and analysis results in unique findings for an actual future satellite mission, that is to be launched soon.
Finite-Horizon Strategy in Infinite-Horizon Linear-Quadratic Discrete-Time Dynamic Games
Authors: Shengyuan Huang, Xiaoguang Yang, Yifen Mu, Wenjun Mei
Date: 2025-06-24 00:00:00

This paper explores a finite-horizon strategy, ``watching $T$ steps into the future and moving one step now,'' in an $N$-person infinite-horizon discrete-time linear-quadratic dynamic game. The game involves linear input/output/state dynamics and quadratic cost functions with heterogeneous discount factors. For the finite-horizon version, which forms the basis of the infinite-horizon game, we analyze the structure of the coupled generalized discrete Riccati difference equations related to the feedback Nash equilibrium (FNE) and derive a sufficient condition for the uniqueness of the finite-horizon FNE. Under this condition, the FNE can be efficiently computed via the proposed algorithm. In the infinite-horizon game, assume all players adopt this finite-horizon strategy. If the iterations of the coupled equations related to the FNE converge, and the invertibility and stability conditions hold, we prove the convergence of each player's total cost under the finite-horizon strategy, even when players use individual prediction horizons. Furthermore, we provide an explicit upper bound on the cost difference between the finite-horizon strategy and the infinite-horizon FNE associated with the limiting matrices, expressed via the distance between their feedback strategy matrices. This bound vanishes as $T$ tends to infinity, implying convergence to the infinite-horizon FNE cost. A non-scalar numerical example illustrates the convergence behavior.
Time-Constrained Interception of Seeker-Equipped Interceptors with Bounded Input
Authors: Ashok Samrat R, Swati Singh, Shashi Ranjan Kumar
Date: 2025-06-24 00:00:00

This paper presents a nonlinear guidance scheme designed to achieve precise interception of stationary targets at a pre-specified impact time. The proposed strategy essentially accounts for the constraints imposed by the interceptor's seeker field-of-view (FOV) and actuator limitations, which, if ignored, can degrade guidance performance. To address these challenges, the guidance law incorporates known actuator bounds directly into its design, thereby improving overall interceptor effectiveness. The proposed method utilizes an input-affine magnitude saturation model to effectively enforce these constraints. By appending this input saturation model to the interceptor's kinematic equations, a guidance law is derived that ensures interception at the desired impact time while accounting for the physical constraints of the sensor and actuator. The efficacy of the proposed strategies is demonstrated through comprehensive numerical simulations across various scenarios and is compared against an existing guidance strategy.
Coherent and Noncoherent Detection in Dense Arrays: Can We Ignore Mutual Coupling?
Authors: Aniol Martí, Luca Sanguinetti, Jaume Riba, Meritxell Lamarca
Date: 2025-06-24 00:00:00

This paper investigates the impact of mutual coupling on MIMO systems with densely deployed antennas. Leveraging multiport communication theory, we analyze both coherent and noncoherent detection approaches in a single-user uplink scenario where the receiver ignores mutual coupling effects. Simulation results indicate that while coherent detection is generally more accurate, it is highly sensitive to mismatches in the coupling model, leading to severe performance degradation when antennas are closely spaced, to the point of becoming unusable. Noncoherent detection, on the other hand, exhibits a higher error probability but is more robust to coupling model mismatches.
Enhanced Fault Ride-Through Grid Forming with Transient Synchronisation Stability and Current Saturation
Authors: Youcefa Brahim Elkhalil, Nima Tashakor, Davood Keshavarzi, Ehsan Asadi, Stefan Goetz
Date: 2025-06-24 00:00:00

During grid faults, grid-forming converters are typically suggested to switch from a voltage-source to a current-source mode to limit the current and protect the electronics. This transition has the potential for the converter to transiently lose synchronization due to such current saturation. Therefore, this paper proposes an alternative current saturation algorithm to improve transient synchronization stability during mode switching. The algorithm is designed for grid-forming converters to meet low-voltage ride-through (LVRT) requirements and grid-fault standards in addition to transient synchronization stability. Moreover, it limits the converter output current during grid faults with a new control parameter. The presented method introduces converter output virtual fluxes to calculate the current references in the d- and q-axes for the current saturation algorithm to enhance LVRT performance and grid stability. The method exploits the correlation between the converter's virtual fluxes and currents to modify the current saturation levels through real-time converter virtual flux estimation. The adaptive saturation levels ensure precise control and high dynamics during grid faults and facilitate optimal power injection or absorption to support the grid. The proposed current-saturation algorithm is analytically evaluated. Further, hardware-in-the-loop (HIL) experiments validate the effectiveness of the proposed algorithm.
Beam Squint Mitigation in Wideband Hybrid Beamformers: Full-TTD, Sparse-TTD, or Non-TTD?
Authors: Mehdi Monemi, Mehdi Rasti, Omid Yazdani, Onel Lopez, Matti Latva-aho
Date: 2025-06-24 00:00:00

Beam squint poses a fundamental challenge in wideband hybrid beamforming, particularly for mmWave and THz systems that demand both ultra-wide bandwidth and high directional beams. While conventional phase shifter-based beamformers may offer partial mitigation, True Time Delay (TTD) units provide a fundamentally more effective solution by enabling frequency-independent beam steering. However, the high cost of TTD units has recently driven much interest in Sparse-TTD architectures, which combine a limited number of TTDs with a higher number of conventional phase shifters to balance performance and cost. This paper provides a critical examination of beam squint mitigation strategies in wideband hybrid beamformers, comparing Full-TTD, Sparse-TTD, and Non-TTD architectures. We analyze recent Non-TTD approaches, specifically the scheme leveraging the wideband beam gain (WBBG) concept, evaluating their performance and cost characteristics against TTD-based solutions. A key focus is placed on the practical limitations of Sparse-TTD architectures, particularly the often-overlooked requirement for wideband phase shifters operating alongside TTDs, which can significantly impact performance and implementation cost in real-world scenarios, especially for ultra-wideband applications. Finally, we conduct a cost-performance analysis to examine the trade-offs inherent in each architecture and provide guidance on selecting the most suitable hybrid beamforming structure for various fractional bandwidth regimes.
Revisiting Power System Stabilizers with Increased Inverter-Based Generation: A Case Study
Authors: Jovan Krajacic, Keith Moffat, Gustavo Valverde
Date: 2025-06-24 00:00:00

As power systems evolve with increasing production from Inverter-Based Resources (IBRs), their underlying dynamics are undergoing significant changes that can jeopardize system operation, leading to poorly damped oscillations or small-signal rotor angle instability. In this work, we investigate whether Power System Stabilizer (PSS) setting adjustments can effectively restore system stability and provide adequate damping in systems with increased IBR penetration, using the benchmark Kundur Two-Area System as a case study. Specifically, we evaluate the model-based Residues and P-Vref PSS tuning methods to examine their effectiveness under evolving grid conditions. Our findings indicate that the effectiveness of these tuning methods is not guaranteed, particularly when coordination is limited. Consequently, our case study motivates local and adaptive online PSS tuning methods.
Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution Grids
Authors: Sarra Bouchkati, Ramil Sabirov, Steffen Kortmann, Andreas Ulbig
Date: 2025-06-24 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper introduces an efficient Residual Reinforcement Learning (RRL) framework for voltage control in active distribution grids. Voltage control remains a critical challenge in distribution grids, where conventional Reinforcement Learning (RL) methods often suffer from slow training convergence and inefficient exploration. To overcome these challenges, the proposed RRL approach learns a residual policy on top of a modified Sequential Droop Control (SDC) mechanism, ensuring faster convergence. Additionally, the framework introduces a Local Shared Linear (LSL) architecture for the Q-network and a Transformer-Encoder actor network, which collectively enhance overall performance. Unlike several existing approaches, the proposed method relies solely on inverters' measurements without requiring full state information of the power grid, rendering it more practical for real-world deployment. Simulation results validate the effectiveness of the RRL framework in achieving rapid convergence, minimizing active power curtailment, and ensuring reliable voltage regulation.
Peer-to-Peer Energy Markets With Uniform Pricing: A Dynamic Operating Envelope Approach
Authors: Zeinab Salehi, Yijun Chen, Ian R. Petersen, Guodong Shi, Duncan S. Callaway, Elizabeth L. Ratnam
Date: 2025-06-24 00:00:00
Tasks: Fairness

The recent widespread adoption of rooftop solar backed by battery storage is enabling energy customers to both produce and consume electricity (i.e., prosumers of electricity). To facilitate prosumer participation in the electric grid, new market mechanisms are required. In this paper, we design peer-to-peer energy markets where prosumers trade their excess energy with peers to gain profit while satisfying the overall balance in electricity supply and demand. We first consider a market structure, considering the case where voltage and/or thermal constraints are binding. When such grid constraints are binding, market clearing prices can vary across locations. However, heterogeneous prices may be considered by regulators to lack fairness. To ensure uniform pricing, we design two peer-to-peer energy markets with dynamic operating envelopes (DOEs). DOEs enable us to decompose global voltage and thermal constraints across the power grid into local constraints for each prosumer, resulting in uniform prices across the grid. By means of numerical simulations on an IEEE 13-node feeder, we benchmark the proposed market-based approaches in the presence of binding voltage constraints.
Online Algorithms for Recovery of Low-Rank Parameter Matrix in Non-stationary Stochastic Systems
Authors: Yanxin Fu, Junbao Zhou, Yu Hu, Wenxiao Zhao
Date: 2025-06-24 00:00:00

This paper presents a two-stage online algorithm for recovery of low-rank parameter matrix in non-stationary stochastic systems. The first stage applies the recursive least squares (RLS) estimator combined with its singular value decomposition to estimate the unknown parameter matrix within the system, leveraging RLS for adaptability and SVD to reveal low-rank structure. The second stage introduces a weighted nuclear norm regularization criterion function, where adaptive weights derived from the first-stage enhance low-rank constraints. The regularization criterion admits an explicit and online computable solution, enabling efficient online updates when new data arrive without reprocessing historical data. Under the non-stationary and the non-persistent excitation conditions on the systems, the algorithm provably achieves: (i) the true rank of the unknown parameter matrix can be identified with a finite number of observations, (ii) the values of the matrix components can be consistently estimated as the number of observations increases, and (iii) the asymptotical normality of the algorithm is established as well. Such properties are termed oracle properties in the literature. Numerical simulations validate performance of the algorithm in estimation accuracy.
Simulation of a closed-loop dc-dc converter using a physics-informed neural network-based model
Authors: Marc-Antoine Coulombe, Maxime Berger, Antoine Lesage-Landry
Date: 2025-06-23 00:00:00

The growing reliance on power electronics introduces new challenges requiring detailed time-domain analyses with fast and accurate circuit simulation tools. Currently, commercial time-domain simulation software are mainly relying on physics-based methods to simulate power electronics. Recent work showed that data-driven and physics-informed learning methods can increase simulation speed with limited compromise on accuracy, but many challenges remain before deployment in commercial tools can be possible. In this paper, we propose a physics-informed bidirectional long-short term memory neural network (BiLSTM-PINN) model to simulate the time-domain response of a closed-loop dc-dc boost converter for various operating points, parameters, and perturbations. A physics-informed fully-connected neural network (FCNN) and a BiLSTM are also trained to establish a comparison. The three methods are then compared using step-response tests to assess their performance and limitations in terms of accuracy. The results show that the BiLSTM-PINN and BiLSTM models outperform the FCNN model by more than 9 and 4.5 times, respectively, in terms of median RMSE. Their standard deviation values are more than 2.6 and 1.7 smaller than the FCNN's, making them also more consistent. Those results illustrate that the proposed BiLSTM-PINN is a potential alternative to other physics-based or data-driven methods for power electronics simulations.