pwc-archive/papers-with-abstracts huggingface dataset:
Sensitivity Analysis of State Space Models for Scrap Composition Estimation in EAF and BOF
Authors: Yiqing Zhou, Karsten Naert, Dirk Nuyens
Date: 2025-04-15 00:00:00
Tasks: ARC, Sensitivity, State Space Models

This study develops and analyzes linear and nonlinear state space models for estimating the elemental composition of scrap steel used in steelmaking, with applications to Electric Arc Furnace (EAF) and Basic Oxygen Furnace (BOF) processes. The models incorporate mass balance equations and are fitted using a modified Kalman filter for linear cases and the Unscented Kalman Filter (UKF) for nonlinear cases. Using Cu and Cr as representative elements, we assess the sensitivity of model predictions to measurement noise in key process variables, including steel mass, steel composition, scrap input mass, slag mass, and iron oxide fraction in slag. Results show that the models are robust to moderate noise levels in most variables, particularly when errors are below $10\%$. However, accuracy significantly deteriorates with noise in slag mass estimation. These findings highlight the practical feasibility and limitations of applying state space models for real-time scrap composition estimation in industrial settings.
Balancing hydrogen delivery in national energy systems: impact of the temporal flexibility of hydrogen delivery on export prices
Authors: Hazem Abdel-Khalek, Eddy Jalbout, Caspar Schauß, Benjamin Pfluger
Date: 2025-04-15 00:00:00

Hydrogen is expected to play a key role in the energy transition. Analyses exploring the price of hydrogen usually calculate average or marginal production costs regardless of the time of delivery. A key factor that affects the price of hydrogen is the balancing costs, which we define as the expense of ensuring a steady schedule of hydrogen delivery. We explore the effect of delivering hydrogen to the export ports at different schedules, ranging from fully flexible to moderately stable with a daily and weekly buffer, to fully stable. We quantify the rise in hydrogen price with strict balancing constraint in three countries: Brazil, Morocco and Turkey, and three export volumes: 10, 50 and 200 TWh. The price difference between the flexible and stable schedules was found to reach a maximum of 36% in Brazil, 47% in Morocco and 18% in Turkey across the different export volumes.
Robust MPC for Uncertain Linear Systems -- Combining Model Adaptation and Iterative Learning
Authors: Hannes Petrenz, Johannes Köhler, Francesco Borrelli
Date: 2025-04-15 00:00:00
Tasks: Computational Efficiency, Model Predictive Control

This paper presents a robust adaptive learning Model Predictive Control (MPC) framework for linear systems with parametric uncertainties and additive disturbances performing iterative tasks. The approach iteratively refines the parameter estimates using set membership estimation. Performance enhancement over iterations is achieved by learning the terminal cost from data. Safety is enforced using a terminal set, which is also learned iteratively. The proposed method guarantees recursive feasibility, constraint satisfaction, and a robust bound on the closed-loop cost. Numerical simulations on a mass-spring-damper system demonstrate improved computational efficiency and control performance compared to an existing robust adaptive MPC approach.
A mixed-integer framework for analyzing neural network-based controllers for piecewise affine systems with bounded disturbances
Authors: Dieter Teichrib, Moritz Schulze Darup
Date: 2025-04-15 00:00:00

We present a method for representing the closed-loop dynamics of piecewise affine (PWA) systems with bounded additive disturbances and neural network-based controllers as mixed-integer (MI) linear constraints. We show that such representations enable the computation of robustly positively invariant (RPI) sets for the specified system class by solving MI linear programs. These RPI sets can subsequently be used to certify stability and constraint satisfaction. Furthermore, the approach allows to handle non-linear systems based on suitable PWA approximations and corresponding error bounds, which can be interpreted as the bounded disturbances from above.
Steering Feedback in Dynamic Driving Simulators: Road-Induced and Non-Road-Induced Harshness
Authors: Maximilian Böhle, Bernhard Schick, Steffen Müller
Date: 2025-04-15 00:00:00

Steering feedback plays a substantial role in the validity of driving simulators for the virtual development of modern vehicles. Established objective steering characteristics typically assess the feedback behavior in the frequency range of up to 30 Hz while factors such as steering wheel and vehicle body vibrations at higher frequencies are mainly approached as comfort issues. This work investigates the influence of steering wheel and vehicle body excitations in the frequency range between 30 and 100 Hz on the subjective evaluation of steering feedback in a dynamic driving simulator. A controlled subject study with 42 participants was performed to compare a reference vehicle with an electrical power steering system to four variants of its virtual representation on a dynamic driving simulator. The effects of road-induced excitations were investigated by comparing a semi-empirical and a physics-based tire model, while the influence of non-road-induced excitations was investigated by implementing engine and wheel orders. The simulator variants were evaluated in comparison to the reference vehicle during closed-loop driving on a country road in a single-blind within-subjects design. The subjective evaluation focused on the perception of road feedback compared to the reference vehicle. The statistical analysis of subjective results shows that there is a strong effect of non-road-induced steering and vehicle body excitations, while the effect of road-induced excitations is considerably less pronounced.
A moving horizon estimator for aquifer thermal energy storages
Authors: Johannes van Randenborgh, Moritz Schulze Darup
Date: 2025-04-14 00:00:00
Tasks: Model Predictive Control

Aquifer thermal energy storages (ATES) represent groundwater saturated aquifers that store thermal energy in the form of heated or cooled groundwater. Combining two ATES, one can harness excess thermal energy from summer (heat) and winter (cold) to support the building's heating, ventilation, and air conditioning (HVAC) technology. In general, a dynamic operation of ATES throughout the year is beneficial to avoid using fossil fuel-based HVAC technology and maximize the ``green use'' of ATES. Model predictive control (MPC) with an appropriate system model may become a crucial control approach for ATES systems. Consequently, the MPC model should reflect spatial temperature profiles around ATES' boreholes to predict extracted groundwater temperatures accurately. However, meaningful predictions require the estimation of the current state of the system, as measurements are usually only at the borehole of the ATES. In control, this is often realized by model-based observers. Still, observing the state of an ATES system is non-trivial, since the model is typically hybrid. We show how to exploit the specific structure of the hybrid ATES model and design an easy-to-solve moving horizon estimator based on a quadratic program.
Distributed Optimization with Gradient Tracking over Heterogeneous Delay-Prone Directed Networks
Authors: Evagoras Makridis, Gabriele Oliva, Kasagatta Ramesh Narahari, Mohammadreza Doostmohammadian, Usman A. Khan, Themistoklis Charalambous
Date: 2025-04-15 00:00:00
Tasks: Distributed Optimization

In this paper, we address the distributed optimization problem over unidirectional networks with possibly time-invariant heterogeneous bounded transmission delays. In particular, we propose a modified version of the Accelerated Distributed Directed OPTimization (ADD-OPT) algorithm, herein called Robustified ADD-OPT (R-ADD-OPT), which is able to solve the distributed optimization problem, even when the communication links suffer from heterogeneous but bounded transmission delays. We show that if the gradient step-size of the R-ADD-OPT algorithm is within a certain range, which also depends on the maximum time delay in the network, then the nodes are guaranteed to converge to the optimal solution of the distributed optimization problem. The range of the gradient step-size that guarantees convergence can be computed a priori based on the maximum time delay in the network.
A Linear Push-Pull Average Consensus Algorithm for Delay-Prone Networks
Authors: Evagoras Makridis, Themistoklis Charalambous
Date: 2025-04-15 00:00:00

In this paper, we address the average consensus problem of multi-agent systems for possibly unbalanced and delay-prone networks with directional information flow. We propose a linear distributed algorithm (referred to as RPPAC) that handles asynchronous updates and time-varying heterogeneous information delays. Our proposed distributed algorithm utilizes a surplus-consensus mechanism and information regarding the number of incoming and outgoing links to guarantee state averaging, despite the imbalanced and delayed information flow in directional networks. The convergence of the RPPAC algorithm is examined using key properties of the backward product of time-varying matrices that correspond to different snapshots of the directional augmented network.
Offset-free Nonlinear MPC with Koopman-based Surrogate Models
Authors: Irene Schimperna, Lea Bold, Karl Worthmann
Date: 2025-04-15 00:00:00
Tasks: Model Predictive Control

In this paper, we design offset-free nonlinear Model Predictive Control (MPC) for surrogate models based on Extended Dynamic Mode Decomposition (EDMD). The model used for prediction in MPC is augmented with a disturbance term, that is estimated by an observer. If the full information about the equilibrium of the real system is not available, a reference calculator is introduced in the algorithm to compute the MPC state and input references. The control algorithm guarantees offset-free tracking of the controlled output under the assumption that the modeling errors are asymptotically constant. The effectiveness of the proposed approach is showcased with numerical simulations for two popular benchmark systems: the van-der-Pol oscillator and the four-tanks process.
Virtual Contraction Approach to Decentralized Adaptive Stabilization of Nonlinear Time-Delayed Networks
Authors: Yu Kawano, Zhiyong Sun
Date: 2025-04-15 00:00:00

In this paper, we utilize a diagonally dominant structure for the decentralized stabilization of unknown nonlinear time-delayed networks. Generalizing the idea of virtual contraction analysis to time-delayed systems, we demonstrate that nonlinear time-delayed networks can be stabilized by diagonal high-gains if the input matrices possess certain generalized (column/row) diagonally dominant properties. To achieve stabilization of unknown networks, we further propose a distributed adaptive tuning rule for each individual gain function, ensuring that all closed-loop trajectories converge to the origin. The effectiveness of the proposed decentralized adaptive control is verified in a case study on epidemic spreading control in SIS networks with transmission delays.
Adaptive Synaptogenesis Implemented on a Nanomagnetic Platform
Authors: Faiyaz Elahi Mullick, Supriyo Bandyopadhyay, Rob Baxter, Tony J. Ragucci, Avik W. Ghosh
Date: 2025-04-14 00:00:00
Tasks: Edge-computing, Hippocampus, Lifelong learning

The human brain functions very differently from artificial neural networks (ANN) and possesses unique features that are absent in ANN. An important one among them is "adaptive synaptogenesis" that modifies synaptic weights when needed to avoid catastrophic forgetting and promote lifelong learning. The key aspect of this algorithm is supervised Hebbian learning, where weight modifications in the neocortex driven by temporal coincidence are further accepted or vetoed by an added control mechanism from the hippocampus during the training cycle, to make distant synaptic connections highly sparse and strategic. In this work, we discuss various algorithmic aspects of adaptive synaptogenesis tailored to edge computing, demonstrate its function using simulations, and design nanomagnetic hardware accelerators for specific functions of synaptogenesis.
Vehicle Dynamics Control for Simultaneous Optimization of Tire Emissions and Performance in EVs
Authors: Chi-Bach Pham, Homayoun Hamedmoghadam Rafati, Robert Noel Shorten
Date: 2025-04-14 00:00:00

In recent years, Electric Vehicles (EVs) have seen widespread public adoption. While EVs produce zero tailpipe emissions, they contribute to an increase in another type of vehicular emission: tire emissions. Battery-operated EVs are generally heavier than their combustion-engine counterparts and require greater acceleration forces, which their high-torque electric motors provide. This combination of increased weight and traction forces leads to higher tire emissions, which possess various adverse health and environmental effects. Here, we propose a control solution with promising results in mitigating tire wear in all-wheel-drive EVs. The idea is to utilize different tire profiles on each drive axis: a low-wear, low-traction axis and a high-wear, high-traction axis. Derived from detailed mathematical analyses, we propose a simple control scheme to counteract the performance difference from using the low-traction tires. The proposed control mechanism then distributes torque optimally between the two axes, maximizing usage from the low-wear axis and simultaneously maintaining stability and performance by leveraging high-traction tires. Through detailed numerical simulations, we demonstrate that the developed model significantly reduces tire emissions and maintains vehicle drivability and performance.
Spectrum Sharing in STAR-RIS-assisted UAV with NOMA for Cognitive Radio Networks
Authors: Ali Nazari, Ali Olfat
Date: 2025-04-14 00:00:00

As an emerging technology, the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can improve the spectrum efficiency (SE) of primary users (PUs) and secondary users (SUs) in cognitive radio (CR) networks by mitigating the interference of the incident signals. The STAR-RIS-assisted unmanned aerial vehicle (UAV) can fully cover the dynamic environment through high mobility and fast deployment. According to the dynamic air-to-ground channels, the STAR-RIS-assisted UAV may face a challenge configuring their elements' coefficients (i.e., reflecting and transmitting the amplitude and phases). Hence, to meet the requirements of dynamic channel determination with the SE approach, this paper proposes the sum rate maximization of both PUs and SUs through non-orthogonal multiple access in CR network to jointly optimize the trajectory and transmission-reflection beamforming design of the STAR-RIS-assisted UAV, and power allocation. Since the non-convex joint optimization problem includes coupled optimization variables, we develop an alternative optimization algorithm. Simulation results study the impact of: 1) the significant parameters, 2) the performance of different intelligence surface modes and STAR-RIS operating protocols, 3) the joint trajectory and beamforming design with fixed and mobile users, and 4) STAR-RIS capabilities such as mitigating the interference, and how variations in the roles of elements dynamically.
Transfer Learning Assisted XgBoost For Adaptable Cyberattack Detection In Battery Packs
Authors: Sanchita Ghosh, Tanushree Roy
Date: 2025-04-14 00:00:00
Tasks: Transfer Learning

Optimal charging of electric vehicle (EVs) depends heavily on reliable sensor measurements from the battery pack to the cloud-controller of the smart charging station. However, an adversary could corrupt the voltage sensor data during transmission, potentially causing local to wide-scale disruptions. Therefore, it is essential to detect sensor cyberattacks in real-time to ensure secure EV charging, and the developed algorithms must be readily adaptable to variations, including pack configurations. To tackle these challenges, we propose adaptable fine-tuning of an XgBoost-based cell-level model using limited pack-level data to use for voltage prediction and residual generation. We used battery cell and pack data from high-fidelity charging experiments in PyBaMM and `liionpack' package to train and test the detection algorithm. The algorithm's performance has been evaluated for two large-format battery packs under sensor swapping and replay attacks. The simulation results also highlight the adaptability and efficacy of our proposed detection algorithm.
Secure Estimation of Battery Voltage Under Sensor Attacks: A Self-Learning Koopman Approach
Authors: Sanchita Ghosh, Tanushree Roy
Date: 2025-04-14 00:00:00
Tasks: Self-Learning

Cloud-based battery management system (BMS) requires accurate terminal voltage measurement data to ensure optimal and safe charging of Lithium-ion batteries. Unfortunately, an adversary can corrupt the battery terminal voltage data as it passes from the local-BMS to the cloud-BMS through the communication network, with the objective of under- or over-charging the battery. To ensure accurate terminal voltage data under such malicious sensor attacks, this paper investigates a Koopman-based secure terminal voltage estimation scheme using a two-stage error-compensated self-learning feedback. During the first stage of error correction, the potential Koopman prediction error is estimated to compensate for the error accumulation due to the linear approximation of Koopman operator. The second stage of error compensation aims to recover the error amassing from the higher-order dynamics of the Lithium-ion batteries missed by the self-learning strategy. Specifically, we have proposed two different methods for this second stage error compensation. First, an interpretable empirical correction strategy has been obtained using the open circuit voltage to state-of-charge mapping for the battery. Second, a Gaussian process regression-based data-driven method has been explored. Finally, we demonstrate the efficacy of the proposed secure estimator using both empirical and data-driven corrections.
Layered Multirate Control of Constrained Linear Systems
Authors: Charis Stamouli, Anastasios Tsiamis, Manfred Morari, George J. Pappas
Date: 2025-04-14 00:00:00
Tasks: Collision Avoidance, Motion Planning

Layered control architectures have been a standard paradigm for efficiently managing complex constrained systems. A typical architecture consists of: i) a higher layer, where a low-frequency planner controls a simple model of the system, and ii) a lower layer, where a high-frequency tracking controller guides a detailed model of the system toward the output of the higher-layer model. A fundamental problem in this layered architecture is the design of planners and tracking controllers that guarantee both higher- and lower-layer system constraints are satisfied. Toward addressing this problem, we introduce a principled approach for layered multirate control of linear systems subject to output and input constraints. Inspired by discrete-time simulation functions, we propose a streamlined control design that guarantees the lower-layer system tracks the output of the higher-layer system with computable precision. Using this design, we derive conditions and present a method for propagating the constraints of the lower-layer system to the higher-layer system. The propagated constraints are integrated into the design of an arbitrary planner that can handle higher-layer system constraints. Our framework ensures that the output constraints of the lower-layer system are satisfied at all high-level time steps, while respecting its input constraints at all low-level time steps. We apply our approach in a scenario of motion planning, highlighting its critical role in ensuring collision avoidance.
Bayesian Analysis of Interpretable Aging across Thousands of Lithium-ion Battery Cycles
Authors: Marc D. Berliner, Minsu Kim, Xiao Cui, Vivek N. Lam, Patrick A. Asinger, Martin Z. Bazant, William C. Chueh, Richard D. Braatz
Date: 2025-04-14 00:00:00
Tasks: Diagnostic, Uncertainty Quantification

The Doyle-Fuller-Newman (DFN) model is a common mechanistic model for lithium-ion batteries. The reaction rate constant and diffusivity within the DFN model are key parameters that directly affect the movement of lithium ions, thereby offering explanations for cell aging. This work investigates the ability to uniquely estimate each electrode's diffusion coefficients and reaction rate constants of 95 Tesla Model 3 cells with a nickel cobalt aluminum oxide (NCA) cathode and silicon oxide--graphite (LiC$_\text{6}$--SiO$_{\text{x}}$) anode. The parameters are estimated at intermittent diagnostic cycles over the lifetime of each cell. The four parameters are estimated using Markov chain Monte Carlo (MCMC) for uncertainty quantification (UQ) for a total of 7776 cycles at discharge C-rates of C/5, 1C, and 2C. While one or more anode parameters are uniquely identifiable over every cell's lifetime, cathode parameters become identifiable at mid- to end-of-life, indicating measurable resistive growth in the cathode. The contribution of key parameters to the state of health (SOH) is expressed as a power law. This model for SOH shows a high consistency with the MCMC results performed over the overall lifespan of each cell. Our approach suggests that effective diagnosis of aging can be achieved by predicting the trajectories of the parameters contributing to cell aging. As such, extending our analysis with more physically accurate models building on DFN may lead to more identifiable parameters and further improved aging predictions.
Online Model Order Reduction of Linear Systems via $(γ,δ)$-Similarity
Authors: Shivam Bajaj, Carolyn L. Beck, Vijay Gupta
Date: 2025-04-14 00:00:00

Model order reduction aims to determine a low-order approximation of high-order models with least possible approximation errors. For application to physical systems, it is crucial that the reduced order model (ROM) is robust to any disturbance that acts on the full order model (FOM) -- in the sense that the output of the ROM remains a good approximation of that of the FOM, even in the presence of such disturbances. In this work, we present a framework for online model order reduction for a class of continuous-time linear systems that ensures this property for any $\mathcal{L}_2$ disturbance. Apart from robustness to disturbances in this sense, the proposed framework also displays other desirable properties for model order reduction: (1) a provable bound on the error defined as the $L_2$ norm of the difference between the output of the ROM and FOM, (2) preservation of stability, (3) compositionality properties and a provable error bound for arbitrary interconnected systems, (4) a provable bound on the output of the FOM when the controller designed for the ROM is used with the FOM, and finally, (5) compatibility with existing approaches such as balanced truncation and moment matching. Property (4) does not require computation of any gap metric and property (5) is beneficial as existing approaches can also be equipped with some of the preceding properties. The theoretical results are corroborated on numerical case studies, including on a building model.