pwc-archive/papers-with-abstracts huggingface dataset:
Graphene-Assisted Chemical Stabilization of Liquid Metal Nano Droplets for Liquid Metal Based Energy Storage
Authors: Afsaneh L. Sanati, Timur Nikitin, Rui Fausto, Carmel Majidi, Mahmoud Tavakoli
Date: 2025-01-27 00:00:00

Energy storage devices with liquid_metal electrodes have attracted interest in recent years due to their potential for mechanical resilience, self_healing, dendrite_free operation, and fast reaction kinetics. Gallium alloys like Eutectic Gallium Indium (EGaIn) are appealing due to their low melting point and high theoretical specific capacity. However, EGaIn electrodes are unstable in highly alkaline electrolytes due to Gallium oxide dissolution. In this letter, this bottleneck is addressed by introducing chemically stable films in which nanoscale droplets of EGaIn are coated with trace amounts of graphene oxide (GO). It is demonstrated that a GO to EGaIn weight ratio as low as 0.01 provides enough protection for a thin film formed by GO EGaIn nanocomposite against significantly acidic or alkaline environments (pH 1-14). It is shown that GO coating significantly enhances the surface stability in such environments, thus improving the energy storage capacity by over 10x. Microstructural analysis confirms GO EGaIn composite stability and enhanced electrochemical performance. Utilizing this, a thin film supercapacitor is fabricated. Results indicate that when coating the EGaIn with GO to EGaIn ratio of 0.001 the areal capacitance improves by 10 times, reaching 20.02 mF cm_2. This breakthrough paves the way for advanced liquid metal-based thin film electrodes, promising significant improvements in energy storage applications.
Impact of Lead Time on Aggregate EV Flexibility for Congestion Management Services
Authors: Nanda Kishor Panda, Peter Palensky, Simon H. Tindemans
Date: 2025-01-27 00:00:00
Tasks: Management

Increased electrification of energy end-usage can lead to network congestion during periods of high consumption. Flexibility of loads, such as aggregate smart charging of Electric Vehicles (EVs), is increasingly leveraged to manage grid congestion through various market-based mechanisms. Under such an arrangement, this paper quantifies the effect of lead time on the aggregate flexibility of EV fleets. Simulations using real-world charging transactions spanning over different categories of charging stations are performed for two flexibility products (redispatch and capacity limitations) when offered along with different business-as-usual (BAU) schedules. Results show that the variation of tradable flexibility depends mainly on the BAU schedules, the duration of the requested flexibility, and its start time. Further, the implication of these flexibility products on the average energy costs and emissions is also studied for different cases. Simulations show that bidirectional (V2G) charging outperforms unidirectional smart charging in all cases.
Stabilization of an unstable reaction-diffusion PDE with input delay despite state and input quantization
Authors: Florent Koudohode, Nikolaos Bekiaris-Liberis
Date: 2025-01-27 00:00:00
Tasks: Quantization

We solve the global asymptotic stability problem of an unstable reaction-diffusion Partial Differential Equation (PDE) subject to input delay and state quantization developing a switched predictor-feedback law. To deal with the input delay, we reformulate the problem as an actuated transport PDE coupled with the original reaction-diffusion PDE. Then, we design a quantized predictor-based feedback mechanism that employs a dynamic switching strategy to adjust the quantization range and error over time. The stability of the closed-loop system is proven properly combining backstepping with a small-gain approach and input-to-state stability techniques, for deriving estimates on solutions, despite the quantization effect and the system's instability. We also extend this result to the input quantization case.
Asynchronous distributed collision avoidance with intention consensus for inland autonomous ships
Authors: Hoang Anh Tran, Nikolai Lauvås, Tor Arne Johansen, Rudy R. Negenborn
Date: 2025-01-27 00:00:00
Tasks: Collision Avoidance, Model Predictive Control

This paper focuses on the problem of collaborative collision avoidance for autonomous inland ships. Two solutions are provided to solve the problem in a distributed manner. We first present a distributed model predictive control (MPC) algorithm that allows ships to directly negotiate their intention to avoid collision in a synchronous communication framework. Moreover, we introduce a new approach to shape the ship's behavior to follow the waterway traffic regulations. The conditional convergence toward a stationary solution of this algorithm is guaranteed by the theory of the Alternating Direction Method of Multipliers (ADMM). To overcome the problem of asynchronous communication between ships, we adopt a new asynchronous nonlinear ADMM and present an asynchronous distributed MPC algorithm based on it. Several simulations and field experiments show that the proposed algorithms can prevent ship collisions even in complex scenarios.
MPC4RL -- A Software Package for Reinforcement Learning based on Model Predictive Control
Authors: Dirk Reinhardt, Katrin Baumgärnter, Jonathan Frey, Moritz Diehl, Sebastien Gros
Date: 2025-01-27 00:00:00
Tasks: Model Predictive Control, Reinforcement Learning (RL)

In this paper, we present an early software integrating Reinforcement Learning (RL) with Model Predictive Control (MPC). Our aim is to make recent theoretical contributions from the literature more accessible to both the RL and MPC communities. We combine standard software tools developed by the RL community, such as Gymnasium, stable-baselines3, or CleanRL with the acados toolbox, a widely-used software package for efficient MPC algorithms. Our core contribution is MPC4RL, an open-source Python package that supports learning-enhanced MPC schemes for existing acados implementations. The package is designed to be modular, extensible, and user-friendly, facilitating the tuning of MPC algorithms for a broad range of control problems. It is available on GitHub.
Mode Switching-Induced Instability of Multi-source Feed DC Microgrid
Authors: Shanshan Jiang, Zelin Sun, Jiankun Zhang, Hua Geng
Date: 2025-01-27 00:00:00
Tasks: Management, Scheduling

In DC microgrids (DCMGs), DC-bus signaling based control strategy is extensively used for power management, where mode switching plays a crucial role in achieving multi-source coordination. However, few studies have noticed the impact of mode switching and switching strategies on system voltage stability. To fill this gap, this paper aims to provide a general analysis framework for mode switching-induced instability in multi-source DCMGs. First, manifold theory is employed to analyze the stability of the DCMG switched system. Subsequently, the instability mechanism and its physical interpretation are explored. The positive feedback activated by the decreasing DC bus voltage during the switching process leads to instability. Switching strategy may inadvertently contribute to this instability. To improve stability, a novel control method based on mode scheduling is proposed, by adjusting switching strategy and thereby correcting the system trajectory. Finally, both real-time simulations and experimental tests on a DCMG system verify the correctness and effectiveness of theoretical analysis results.
Intuition and importance of feedback control through laboratory experiences
Authors: Aldo Jonathan Munoz-Vazquez
Date: 2025-01-27 00:00:00
Tasks: Friction

This work aims to raise awareness among engineering students from different disciplines on the importance of feedback control. The proposal consists in comparing the performance of different control strategies in a laboratory session, considering Matlab/Simulink simulations of the non-linear pendulum model. First, students attempt to make the pendulum stop at unstable equilibrium by controlling the torque input with a joystick connected to the computer via an Arduino board. Different friction scenarios are considered for students to explore the dissipation in the system response. Then, as a second task, the Arduino is used to introduce the position reference, while students implement different control strategies, such as Bang-Bang, PID (proportional-integral-derivative) and FPID (fractional PID) controllers, analyzing the system response by inspecting the signals in a scope and in a 3D animated model. The dynamic model results as an application of the laws of rotational motion, and the control methods are explained from an intuitive point of view, focusing on the meaning and motivation of the control actions, with the intention to develop intuition about PID and FPID control methods.
Nuisance-free Automatic Ground Collision Avoidance System Design: Merging Exponential-CBF and Adaptive Sliding Manifolds
Authors: Ege C. Altunkaya, Ibrahim Ozkol
Date: 2025-01-26 00:00:00
Tasks: Collision Avoidance

The significance of the automatic ground collision avoidance system (Auto-GCAS) has been proven by considering the fatal crashes that have occurred over decades. Even though extensive efforts have been put forth to address the ground collision avoidance in the literature, the notion of being nuisance-free has not been sufficiently addressed. At this point, in this study, the Auto-GCAS design is formulated by merging exponential control barrier functions with sliding manifolds to manipulate the barrier function dynamics. The adaptive properties of the sliding manifolds are tailored to the key and governing flight parameters, ensuring that the nuisance-free requirement is satisfied. Furthermore, to ensure all safety requirements are met, a flight envelope protection algorithm is designed using control barrier functions to assess the commands generated by the Auto-GCAS. Eventually, the performance of the proposed methodology is demonstrated, focusing on authority-sharing, collision avoidance capability, and nuisance-free operation through various scenarios and Monte Carlo simulations.
Dynamic Regressor Extension and Mixing-based Re-design of Adaptive Observer for Affine Systems
Authors: Mehdi Tavan
Date: 2025-01-26 00:00:00

The dynamic regressor extension and mixing procedure is employed to redesign a conventional adaptive observer algorithm for affine systems. A reduced-order observer is designed without the construction of the state transition matrix. The dynamics of the regressor are redesigned to incorporate feedback from its extension, transforming the regressor dynamics into a perturbed damped nonlinear oscillator form. This introduces some flexibility in reducing the degradation of parameter convergence due to the lack of the transition matrix and in enhancing the excitation property of the extension matrix.
A Framework for Quasi Time-Optimal Nonlinear Model Predictive Control with Soft Constraints
Authors: Joe Ismail, Steven Liu
Date: 2020-05-06 00:00:00
Tasks: Model Predictive Control

In many mechatronic applications, controller input costs are negligible and time optimality is of great importance to maximize the productivity by executing fast positioning maneuvers. As a result, the obtained control input has mostly a bang-bang nature, which excite undesired mechanical vibrations, especially in systems with flexible structures. This paper tackles the time-optimal control problem and proposes a novel approach, which explicitly addresses the vibrational behavior in the context of the receding horizon technique. Such technique is a key feature, especially for systems with a time-varying vibrational behavior. In the context of model predictive control (MPC), vibrational behavior is predicted and coped in a soft-constrained formulation, which penalize any violation of undesired vibrations. This formulation enlarges the feasibility on a wide operating range in comparison with a hard-constrained formulation. The closed-loop performance of this approach is demonstrated on a numerical example of stacker crane with high degree of flexibility.
DER Hosting capacity for distribution networks: definitions, attributes, use-cases and challenges
Authors: Md Umar Hashmi
Date: 2025-01-25 00:00:00
Tasks: Computational Efficiency

The rapid adoption of distributed energy resources (DERs) has outpaced grid modernization, leading to capacity limitations that challenge their further integration. Hosting Capacity Assessment (HCA) is a critical tool for evaluating how much DER capacity a grid can handle without breaching operational limits. HCA serves multiple goals: enabling higher DER penetration, accelerating grid connection times, guiding infrastructure upgrades or flexible resource deployment, ensuring equitable policies, and improving grid flexibility while minimizing curtailment. HCA lacks a universal definition, varying by modelling approaches, uncertainty considerations, and objectives. This paper addresses five key questions to standardize and enhance HCA practices. First, it classifies HCA objectives associated with different stakeholders such as system operators, consumers, market operators and consumers. Second, it examines model attributes, including modelling sophistication, data requirements, and uncertainty handling, thus balancing complexity with computational efficiency. Third, it explores HCA applications, such as planning grid investments or operational decisions, and summarizes use cases associated with HCA. Fourth, it emphasizes the need for periodic updates to reflect dynamic grid conditions, evolving technologies, and new DER installations. Finally, it identifies challenges, such as ensuring data quality, managing computational demands, and aligning short-term and long-term goals. By addressing these aspects, this paper provides a structured approach to perform and apply HCA, offering insights for engineers, planners, and policymakers to manage DER integration effectively.
On Spectral Approach to the Synthesis of Shaping Filters
Authors: Konstantin A. Rybakov
Date: 2025-01-25 00:00:00
Tasks: Form

This paper describes various approaches to modeling a random process with a given rational power spectral density. The main attention is paid to the spectral form of mathematical description, which allows one to obtain a relation for the shaping filter using a transfer function without any additional calculations. The paper provides all necessary relations for the implementation of the shaping filter based on the spectral form of mathematical description.
Automatic Link Selection in Multi-Channel Multiple Access with Link Failures
Authors: Mevan Wijewardena, Michael J. Neely
Date: 2025-01-24 00:00:00
Tasks: Fairness

This paper focuses on the problem of automatic link selection in multi-channel multiple access control using bandit feedback. In particular, a controller assigns multiple users to multiple channels in a time slotted system, where in each time slot at most one user can be assigned to a given channel and at most one channel can be assigned to a given user. Given that user $i$ is assigned to channel $j$, the transmission fails with a fixed probability $f_{i,j}$. The failure probabilities are not known to the controller. The assignments are made dynamically using success/failure feedback. The goal is to maximize the time average utility, where we consider an arbitrary (possibly nonsmooth) concave and entrywise nondecreasing utility function. The problem of merely maximizing the total throughput has a solution of always assigning the same user-channel pairs and can be unfair to certain users, particularly when the number of channels is less than the number of users. Instead, our scheme allows various types of fairness, such as proportional fairness, maximizing the minimum, or combinations of these by defining the appropriate utility function. We propose two algorithms for this task. The first algorithm is adaptive and gets within $\mathcal{O}(\log(T)/T^{1/3})$ of optimality over any interval of $T$ consecutive slots over which the success probabilities do not change. The second algorithm has faster $\mathcal{O}(\sqrt{\log(T)/T})$ performance over the first $T$ slots, but does not adapt well if probabilities change.
What is a Relevant Signal-to-Noise Ratio for Numerical Differentiation?
Authors: Shashank Verma, Mohammad Almuhaihi, Dennis S. Bernstein
Date: 2025-01-24 00:00:00

In applications that involve sensor data, a useful measure of signal-to-noise ratio (SNR) is the ratio of the root-mean-squared (RMS) signal to the RMS sensor noise. The present paper shows that, for numerical differentiation, the traditional SNR is ineffective. In particular, it is shown that, for a harmonic signal with harmonic sensor noise, a natural and relevant SNR is given by the ratio of the RMS of the derivative of the signal to the RMS of the derivative of the sensor noise. For a harmonic signal with white sensor noise, an effective SNR is derived. Implications of these observations for signal processing are discussed.
Communication-Based Distributed Control of Large-Scale District Heating Networks
Authors: Audrey Blizard, Stephanie Stockar
Date: 2025-01-24 00:00:00

This paper presents a non-cooperative distributed model predictive controller for the control of large-scale District Heating Networks. To enable the design of this controller a novel information passing scheme and feasibility restoration method are created, allowing the local controllers to achieve a global consensus while minimizing a local cost function. The effectiveness of this controller is demonstrated on an 18-user District Heating Network decomposed into six subsystems. The results show that the developed control scheme effectively uses flexibility to manage the buildings' heat demands reducing the total losses by 14% and the return temperature by 37%.
Predictive Position Estimation for Remote Surgery under Packet Loss Using the Informer Framework
Authors: Muhammad Hanif Lashari, Shakil Ahmed, Wafa Batayneh, Ashfaq Khokhar
Date: 2025-01-24 00:00:00
Tasks: Decoder, Position

Accurate and real-time position estimation of the robotic arm on the patient's side is crucial for the success of remote robotic surgery in Tactile Internet environments. This paper proposes a predictive approach using the computationally efficient Transformer-based Informer model for position estimation, combined with a Four-State Hidden Markov Model (4-State HMM) to simulate realistic packet loss scenarios. The method effectively addresses network-induced delays, jitter, and packet loss, ensuring reliable performance in remote robotic surgery. The study evaluates the Informer model on the JIGSAWS dataset, demonstrating its capability to handle sequential data challenges caused by network uncertainties. Key features, including ProbSparse attention and a generative-style decoder, enhance prediction accuracy, computational speed, and memory efficiency. Results indicate that the proposed method achieves over 90 percent accuracy across varying network conditions. Furthermore, the Informer framework outperforms traditional models such as TCN, RNN, and LSTM, highlighting its suitability for real-time remote surgery applications.
A sub-structuring approach for model reduction of frictionally clamped thin-walled structures
Authors: Patrick Hippold, Johann Gross, Malte Krack
Date: 2025-01-24 00:00:00
Tasks: Friction

Thin-walled structures clamped by friction joints, such as aircraft skin panels are exposed to bending-stretching coupling and frictional contact. We propose an original sub-structuring approach, where the system is divided into thin-walled and support regions, so that geometrically nonlinear behavior is relevant only in the former, and nonlinear contact behavior only in the latter. This permits to derive reduced component models, in principle, with available techniques. The Hurty-/Craig-Bampton method, combined with an interface reduction relying on an orthogonal polynomial series, is used to construct the reduction basis for each component. To model geometrically nonlinear behavior, implicit condensation is used, where an original, engineering-oriented proposition is made for the delicate scaling of the static load cases required to estimate the coefficients of the nonlinear terms. The proposed method is validated and its computational performance is assessed for the example of a plate with frictional clamping, using finite element analysis as reference. The numerical results shed light into an interesting mutual interaction: The extent of geometric hardening is limited by the reduced boundary stiffness when more sliding occurs in the clamping. On the other hand, the frictional dissipation is increased by the tangential loading induced by membrane stretching.
A Transferable Physics-Informed Framework for Battery Degradation Diagnosis, Knee-Onset Detection and Knee Prediction
Authors: Huang Zhang, Xixi Liu, Faisal Altaf, Torsten Wik
Date: 2025-01-24 00:00:00
Tasks: Feature Engineering, Onset Detection, Prognosis

The techno-economic and safety concerns of battery capacity knee occurrence call for developing online knee detection and prediction methods as an advanced battery management system (BMS) function. To address this, a transferable physics-informed framework that consists of a histogram-based feature engineering method, a hybrid physics-informed model, and a fine-tuning strategy, is proposed for online battery degradation diagnosis and knee-onset detection. The hybrid model is first developed and evaluated using a scenario-aware pipeline in protocol cycling scenarios and then fine-tuned to create a local model deployed in a dynamic cycling scenario. A 2D histogram-based feature set is found to be the best choice in both source and target scenarios. The fine-tuning strategy is proven to be effective in improving battery degradation mode estimation and degradation phase detection performance in the target scenario. Again, a strong linear correlation was found between the identified knee-onset and knee points. As a result, advanced BMS functions, such as online degradation diagnosis and prognosis, online knee-onset detection and knee prediction, aging-aware battery classification, and second-life repurposing, can be enabled through a battery performance digital twin in the cloud.