pwc-archive/papers-with-abstracts huggingface dataset:
ARM-IRL: Adaptive Resilience Metric Quantification Using Inverse Reinforcement Learning
Authors: Abhijeet Sahu, Venkatesh Venkataramanan, Richard Macwan
Date: 2025-01-21 00:00:00
Tasks: Metric Learning, reinforcement-learning, Reinforcement Learning

Resilience of safety-critical systems is gaining importance, particularly with the increasing number of cyber and physical threats. Cyber-physical threats are becoming increasingly prevalent, as digital systems are ubiquitous in critical infrastructure. The challenge with determining the resilience of cyber-physical systems is identifying a set of resilience metrics that can adapt to the changing states of the system. A static resilience metric can lead to an inaccurate estimation of system state, and can result in unintended consequences against cyber threats. In this work, we propose a data-driven method for adaptive resilience metric learning. The primary goal is to learn a single resilience metric by formulating an inverse reinforcement learning problem that learns a reward or objective from a set of control actions from an expert. It learns the structure or parameters of the reward function based on information provided by expert demonstrations. Most prior work has considered static weights or theories from fuzzy logic to formulate a single resilience metric. Instead, this work learns the resilience metric, represented as reward function, using adversarial inverse reinforcement learning, to determine the optimal policy through training the generator discriminator in parallel. We evaluate our proposed technique in scenarios such as optimal communication network rerouting, power distribution network reconfiguration, and a combined cyber-physical restoration of critical load using the IEEE 123-bus system.
Microgrid Operation Control with State-of-Charge- Dependent Storage Power Constraints
Authors: E. D. Gomez Anccas, C. A. Hans, D. Schulz
Date: 2025-01-21 00:00:00
Tasks: Model Predictive Control

The microgrid concept offers high flexibility and resilience due to the possibility of switching between grid-connected and stand-alone operation. This renders microgrids an auspicious solution for rural areas and critical infrastructure. In standalone or islanded mode, the main objective is cost minimization while ensuring a safe and reliable operation. Optimal operation schemes for microgrids usually assume fixed power limits for energy storage units. This, however, is not sufficient for lithiumion energy storage systems, which often come with dynamic power limits that depend on the state of charge. These limits are especially prominent when the state of charge is close to its boundaries. In this paper, dynamic constraints for energy storages are modelled using convex polytopes and fitted to experimental data acquired from an 11.6 kWh lithium-ion energy storage system. The polytopic constraints are integrated in a model predictive control scheme that was designed for a standalone microgrid composed of a fuel cell, a photovoltaic generator and a lithium-ion energy storage system. To evaluate the advantages, a case study with two configurations is performed. The model predictive controller without polytopic constraints led to constraint violations in 11.77 % of the simulation time steps with a maximum deviation of 118 % above the power limits. The configuration with polytopic constraints in contrary led to no violations over the entire simulation horizon.
Characterization of Invariance, Periodic Solutions and Optimization of Dynamic Financial Networks
Authors: Leonardo Stella, Dario Bauso, Franco Blanchini, Patrizio Colaneri
Date: 2025-01-21 00:00:00

Cascading failures, such as bankruptcies and defaults, pose a serious threat for the resilience of the global financial system. Indeed, because of the complex investment and cross-holding relations within the system, failures can occur as a result of the propagation of a financial collapse from one organization to another. While this problem has been studied in depth from a static angle, namely, when the system is at an equilibrium, we take a different perspective and study the corresponding dynamical system. The contribution of this paper is threefold. First, we carry out a systematic analysis of the regions of attraction and invariance of the system orthants, defined by the positive and negative values of the organizations' equity. Second, we investigate periodic solutions and show through a counterexample that there could exist periodic solutions of period greater than 2. Finally, we study the problem of finding the smallest cash injection that would bring the system to the maximal invariant region of the positive orthant.
Comparative Analysis of Control Strategies for Position Regulation in DC Servo Motors
Authors: Raihan Khan Akash
Date: 2025-01-21 00:00:00
Tasks: Position

A servomotor is a closed-loop system designed for precise movement control, utilizing position feedback to achieve accurate final positions. Due to the ability to deliver higher power output and operate at enhanced speeds, DC servo motors are considered ideal for applications requiring precision and performance. This research aims to design, simulate, and compare various control strategies for precise position control in DC servo motors (DSM). The controllers evaluated in this study include proportional (P), proportional-integral (PI), proportional-integral-derivative (PID), state-feedback controllers (SFC), and state-feedback controllers augmented with integral action (SFCIA). The performance of these controllers was evaluated using MATLAB simulations, characterized by overshoot, settling time, steady-state error, rise time, and peak time. The results indicate that the state-feedback controller with integral action (SFCIA) surpasses other control strategies by achieving zero steady-state error, minimal overshoot, the shortest settling time, and optimized rise and peak times. These findings highlight the effectiveness of SFCIA for tasks requiring high levels of stability, precision, and dynamic performance.
Ultra-High Reliability by Predictive Interference Management Using Extreme Value Theory
Authors: Fateme Salehi, Aamir Mahmood, Sinem Coleri, Mikael Gidlund
Date: 2025-01-20 00:00:00
Tasks: Density Estimation, Management, Prediction

Ultra-reliable low-latency communications (URLLC) require innovative approaches to modeling channel and interference dynamics, extending beyond traditional average estimates to encompass entire statistical distributions, including rare and extreme events that challenge achieving ultra-reliability performance regions. In this paper, we propose a risk-sensitive approach based on extreme value theory (EVT) to predict the signal-to-interference-plus-noise ratio (SINR) for efficient resource allocation in URLLC systems. We employ EVT to estimate the statistics of rare and extreme interference values, and kernel density estimation (KDE) to model the distribution of non-extreme events. Using a mixture model, we develop an interference prediction algorithm based on quantile prediction, introducing a confidence level parameter to balance reliability and resource usage. While accounting for the risk sensitivity of interference estimates, the prediction outcome is then used for appropriate resource allocation of a URLLC transmission under link outage constraints. Simulation results demonstrate that the proposed method outperforms the state-of-the-art first-order discrete-time Markov chain (DTMC) approach by reducing outage rates up to 100-fold, achieving target outage probabilities as low as \(10^{-7}\). Simultaneously, it minimizes radio resource usage \(\simnot15 \%\) compared to DTMC, while remaining only \(\simnot20 \%\) above the optimal case with perfect interference knowledge, resulting in significantly higher prediction accuracy. Additionally, the method is sample-efficient, able to predict interference effectively with minimal training data.
Power Ramp-Rate Control via Power Regulation for Storageless Grid-Connected Photovoltaic Systems
Authors: Jose Miguel Riquelme-Dominguez, Francisco de Paula García-López, Sergio Martinez
Date: 2025-01-20 00:00:00

Photovoltaic Power Ramp-Rate Control (PRRC) constitutes a key ancillary service for future power systems. Although its implementation through the installation of storage systems or irradiance sensors has been widely investigated, fewer studies have explored the power curtailment approach. The latter lacks efficiency, as it voluntarily produces power discharges, yet it is a cost-effective solution in terms of capital expenditures. This paper proposes a novel storageless and sensorless photovoltaic PRRC for grid-connected applications in which the photovoltaic power, rather than the voltage, is the controlled magnitude. The aforementioned contribution makes the effective tracking of the power ramp-rate limit possible compared to the existing methods in the literature. The method is assisted by a real-time curve-fitting algorithm that estimates the Maximum Power Point while operating suboptimally. Thus, no direct temperature or irradiance measurement systems are needed. The validation of the proposed PRRC strategy has been tested by simulation and compared to another approach available in the literature, considering real-field highly variable irradiance data. Experimental validation of the proposed strategy has been performed in real time via Controller Hardware-in-the-Loop.
PSO-based Sliding Mode Current Control of Grid-Forming Inverter in Rotating Frame
Authors: Quang-Manh Hoang, Guilherme Vieira Hollweg, Akhtar Hussain, Sina Zarrabian, Wencong Su, Van-Hai Bui
Date: 2025-01-20 00:00:00
Tasks: Metaheuristic Optimization

The Grid-Forming Inverter (GFMI) is an emerging topic that is attracting significant attention from both academic and industrial communities, particularly in the area of control design. The Decoupled Average Model-based Sliding Mode Current Controller (DAM-SMC) has been used to address the need such as fast response, fixed switching frequency, and no overshoot to avoid exceeding current limits. Typically, the control parameters for DAM-SMC are chosen based on expert knowledge and certain assumptions. However, these parameters may not achieve optimized performance due to system dynamics and uncertainties. To address this, this paper proposes a Particle Swarm Optimization (PSO)-based DAM-SMC controller, which inherits the control laws from DAM-SMC but optimizes the control parameters offline using PSO. The main goal is to reduce chattering and achieve smaller tracking errors. The proposed method is compared with other metaheuristic optimization algorithms, such as Genetic Algorithm (GA) and Simulated Annealing (SA). Simulations are performed in MATLAB/Simulink across various scenarios to evaluate the effectiveness of the proposed controller. The proposed approach achieves a substantial reduction in convergence time, decreasing it by 86.36% compared to the GA and by 88.89% compared to SA. Furthermore, the tracking error is reduced by 11.61% compared to the conventional DAM-SMC algorithm. The robustness of the proposed method is validated under critical conditions, where plant and control model parameters varied by up to 40%.
DRL-Based Maximization of the Sum Cross-Layer Achievable Rate for Networks Under Jamming
Authors: Abdul Basit, Muddasir Rahim, Tri Nhu Do, Nadir Adam, Georges Kaddoum
Date: 2025-01-20 00:00:00
Tasks: Deep Reinforcement Learning

In quasi-static wireless networks characterized by infrequent changes in the transmission schedules of user equipment (UE), malicious jammers can easily deteriorate network performance. Accordingly, a key challenge in these networks is managing channel access amidst jammers and under dynamic channel conditions. In this context, we propose a robust learning-based mechanism for channel access in multi-cell quasi-static networks under jamming. The network comprises multiple legitimate UEs, including predefined UEs (pUEs) with stochastic predefined schedules and an intelligent UE (iUE) with an undefined transmission schedule, all transmitting over a shared, time-varying uplink channel. Jammers transmit unwanted packets to disturb the pUEs' and the iUE's communication. The iUE's learning process is based on the deep reinforcement learning (DRL) framework, utilizing a residual network (ResNet)-based deep Q-Network (DQN). To coexist in the network and maximize the network's sum cross-layer achievable rate (SCLAR), the iUE must learn the unknown network dynamics while concurrently adapting to dynamic channel conditions. Our simulation results reveal that, with properly defined state space, action space, and rewards in DRL, the iUE can effectively coexist in the network, maximizing channel utilization and the network's SCLAR by judiciously selecting transmission time slots and thus avoiding collisions and jamming.
Early Stopping Bayesian Optimization for Controller Tuning
Authors: David Stenger, Dominik Scheurenberg, Heike Vallery, Sebastian Trimpe
Date: 2025-01-20 00:00:00
Tasks: Bayesian Optimization

Manual tuning of performance-critical controller parameters can be tedious and sub-optimal. Bayesian Optimization (BO) is an increasingly popular practical alternative to automatically optimize controller parameters from few experiments. Standard BO practice is to evaluate the closed-loop performance of parameters proposed during optimization on an episode with a fixed length. However, fixed-length episodes can be wasteful. For example, continuing an episode where already the start shows undesirable behavior such as strong oscillations seems pointless. Therefore, we propose a BO method that stops an episode early if suboptimality becomes apparent before an episode is completed. Such early stopping results in partial observations of the controller's performance, which cannot directly be included in standard BO. We propose three heuristics to facilitate partially observed episodes in BO. Through five numerical and one hardware experiment, we demonstrate that early stopping BO can substantially reduce the time needed for optimization.
Optimal Trajectory Control of Geometrically Exact Strings with Space-Time Finite Elements
Authors: Tobias Thoma, Paul Kotyczka
Date: 2025-01-20 00:00:00

In this contribution, we present a variational space-time formulation which generates an optimal feed-forward controller for geometrically exact strings. More concretely, the optimization problem is solved with an indirect approach, and the space-time finite element method translates the problem to a set of algebraic equations. Thereby, only the positional field and the corresponding adjoint variable field are approximated by continuous shape functions, which makes the discretization of a velocity field unnecessary. In addition, the variational formulation can be solved using commercial or open source finite element packages. The entire approach can also be interpreted as a multiple-shooting method for solving the optimality conditions based on the semi-discrete problem. The performance of our approach is demonstrated by a numerical test.
Sampled-Data Control using Hermite-Obreschkoff Methods with an IDA-PBC Example
Authors: Le Zhang, Paul Kotyczka
Date: 2025-01-20 00:00:00

The motivation for this paper is the implementation of nonlinear state feedback control, designed based on the continuous-time plant model, in a sampled control loop under relatively slow sampling. In previous work we have shown that using one-step predictions of the target dynamics with higher order integration schemes, together with possibly higher order input shaping, is a simple and effective way to increase the feasible sampling times until performance degradation and instability occur. In this contribution we present a unifying derivation for arbitrary orders of the previously used Lobatto IIIA collocation and Hermite interpolation schemes through the Hermite-Obreschkoff formula. We derive, moreover, an IDA-PBC controller for a magnetic levitation system, which requires a non-constant target interconnection matrix, and show experimental results.
Efficient Reduction of Interconnected Subsystem Models using Abstracted Environments
Authors: Luuk Poort, Bart Besselink, Rob H. B. Fey, Nathan van de Wouw
Date: 2025-01-20 00:00:00

We present two frameworks for structure-preserving model order reduction of interconnected subsystems, improving tractability of the reduction methods while ensuring stability and accuracy bounds of the reduced interconnected model. Instead of reducing each subsystem independently, we take a low-order abstraction of its environment into account to better capture the dynamics relevant to the external input-output behaviour of the interconnected system, thereby increasing accuracy of the reduced interconnected model. This approach significantly reduces the computational costs of reduction by abstracting instead of fully retaining the environment. The two frameworks differ in how they generate these abstracted environments: one abstracts the environment as a whole, whereas the other abstracts each individual subsystem. By relating low-level errors introduced by reduction and abstraction to the resulting high-level error on the interconnected system, we are able to translate high-level accuracy requirements (on the reduced interconnected system) to low-level specifications (on abstraction and reduction errors) using techniques from robust performance analysis. By adhering to these low-level specifications, restricting the introduced low-level errors, both frameworks automatically guarantee the accuracy and stability of the reduced interconnected system. We demonstrate the effectiveness of both frameworks by applying them to a structural dynamics model of a two-stroke wafer stage, achieving improved accuracy and/or greater reduction compared to an existing method from literature.
Linear ADRC is equivalent to PID with set-point weighting and measurement filter
Authors: Fredrik Bagge Carlson
Date: 2025-01-20 00:00:00

We show that linear Active Disturbance-Rejection Control (ADRC) tuned using the "bandwidth method" is equivalent to PI(D) control with set-point weighting and a lowpass filter on the measurement signal. We also provide simple expressions that make it possible to implement linear ADRC for first and second-order systems using commonplace two degree-of-freedom PID implementations. The expressions are equivalent to ADRC in the response from measurements, and a slight approximation in the response from references.
Unlocking the Potential: A Novel Tool for Assessing Untapped Micro-Pumped Hydro Energy Storage Systems in Michigan
Authors: Sharaf K. Magableh, Xuesong Wang, Oraib Dawaghreh, Caisheng Wang
Date: 2025-01-20 00:00:00
Tasks: Data Visualization, Decision Making

This study presents an innovative tool designed to unlock the potential of Michigan's lakes and dams for applications such as water resource management and renewable energy generation. Given Michigan's relatively flat landscape, the focus is on systems that could serve as micro-hydro energy storage solutions. To ensure accuracy and reliability, the tool incorporates extensive data gathered from authorized sources, covering more than 420 water facilities and potential reservoirs in the state. These data are used as part of a case study to evaluate the tool's capabilities. Key parameters assessed include horizontal and vertical distances (head), volume, and the total storage capacity of each reservoir, measured in GWh. By analyzing these factors, the tool determines the suitability of various lakes and dams for hydroelectric power generation, and other uses based on the horizontal and vertical threshold distances. Its robust assessment framework integrates these metrics to comprehensively evaluate each site's potential. The tool's friendly interface and advanced data visualization features make the findings easy to interpret, facilitating optimal resource utilization and informed decision-making for state authorities. Hence, this tool represents a meaningful advancement in managing Michigan's water resources sustainably, promoting environmentally friendly practices, and supporting economic development.
Global Attitude Synchronization for Multi-agent Systems on SO(3)
Authors: Mouaad Boughellaba, Soulaimane Berkane, Abdelhamid Tayebi
Date: 2025-01-19 00:00:00

In this paper, we address the problem of attitude synchronization for a group of rigid body systems evolving on SO(3). The interaction among these systems is modeled through an undirected, connected, and acyclic graph topology. First, we present an almost global continuous distributed attitude synchronization scheme with rigorously proven stability guarantees. Thereafter, we propose two global distributed hybrid attitude synchronization schemes on SO(3). The first scheme is a hybrid control law that leverages angular velocities and relative orientations to achieve global alignment to a common orientation. The second scheme eliminates the dependence on angular velocities by introducing dynamic auxiliary variables, while ensuring global asymptotic attitude synchronization. This velocity-free control scheme relies exclusively on attitude information. Simulation results are provided to illustrate the effectiveness of the proposed distributed attitude synchronization schemes.
Systems Engineering for Autonomous Vehicles; Supervising AI using Large Language Models (SSuperLLM)
Authors: Diomidis Katzourakis
Date: 2025-01-18 00:00:00
Tasks: Autonomous Vehicles, Trajectory Planning

Generative Artificial Intelligence (GAI) and the idea to use hierarchical models has been around for some years now. GAI has proved to be an extremely useful tool for Autonomous Vehicles (AVs). AVs need to perform robustly in their environment. Thus the AV behavior and short-term trajectory planning needs to be: a) designed and architected using safeguarding and supervisory systems and b) verified using proper Systems Engineering (SysEng) Principles. Can AV Systems Engineering also use Large Language Models (LLM) to help Autonomous vehicles (AV) development? This reader-friendly paper advocates the use of LLMs in 1) requirements (Reqs) development and 2) Reqs verification and 3) provides a proof-of-concept of AV supervisory control. The latter uses a simulation environment of a simple planar (bicycle) vehicle dynamics model and a Linear Quadratic Regulator (LQR) control with an LLM Application Interface (API). The Open-Source simulation SW is available from the author accessible to the readers so that they can engage into the AV stack, LLM API and rules, SysEng and Reqs and fundamental vehicle dynamics and control.
Integrating Expert and Physics Knowledge for Modeling Heat Load in District Heating Systems
Authors: Francisco Souza, Thom Badings, Geert Postma, Jeroen Jansen
Date: 2025-01-18 00:00:00
Tasks: energy management, Management

New residential neighborhoods are often supplied with heat via district heating systems (DHS). Improving the energy efficiency of a DHS is critical for increasing sustainability and satisfying user requirements. In this paper, we present HELIOS, a dedicated artificial intelligence (AI) model designed specifically for modeling the heat load in DHS. HELIOS leverages a combination of established physical principles and expert knowledge, resulting in superior performance compared to existing state-of-the-art models. HELIOS is explainable, enabling enhanced accountability and traceability in its predictions. We evaluate HELIOS against ten state-of-the-art data-driven models in modeling the heat load in a DHS case study in the Netherlands. HELIOS emerges as the top-performing model while maintaining complete accountability. The applications of HELIOS extend beyond the present case study, potentially supporting the adoption of AI by DHS and contributing to sustainable energy management on a larger scale.