pwc-archive/papers-with-abstracts huggingface dataset:
IRS Assisted Decentralized Learning for Wideband Spectrum Sensing
Authors: Sicheng Liu, Qun Wang, Zhuwei Qin, Weishan Zhang, Jingyi Wang, Xiang Ma
Date: 2025-04-02 00:00:00
Tasks: Management

The increasing demand for reliable connectivity in industrial environments necessitates effective spectrum utilization strategies, especially in the context of shared spectrum bands. However, the dynamic spectrum-sharing mechanisms often lead to significant interference and critical failures, creating a trade-off between spectrum scarcity and under-utilization. This paper addresses these challenges by proposing a novel Intelligent Reflecting Surface (IRS)-assisted spectrum sensing framework integrated with decentralized deep learning. The proposed model overcomes partial observation constraints and minimizes communication overhead while leveraging IRS technology to enhance spectrum sensing accuracy. Through comprehensive simulations, the framework demonstrates its ability to monitor wideband spectrum occupancy effectively, even under challenging signal-to-noise ratio (SNR) conditions. This approach offers a scalable and robust solution for spectrum management in next-generation wireless networks.
Model-Predictive Planning and Airspeed Regulation to Minimize Flight Energy Consumption
Authors: Trevor Karpinski, Alexander Blakesley, Jakub Krol, Bani Anvari, George Gorospe, Liang Sun
Date: 2025-04-02 00:00:00

Although battery technology has advanced tremendously over the past decade, it continues to be a bottleneck for the mass adoption of electric aircraft in long-haul cargo and passenger delivery. The onboard energy is expected to be utilized in an efficient manner. Energy concumption modeling research offers increasingly accurate mathematical models, but there is scant research pertaining to real-time energy optimization at an operational level. Additionally, few publications include landing and take-off energy demands in their governing models. This work presents fundamental energy equations and proposes a proportional-integral-derivative (PID) controller. The proposed method demonstrates a unique approach to an energy consumption model that tracks real-time energy optimization along a predetermined path. The proposed PID controller was tested in simulation, and the results show its effectiveness and accuracy in driving the actual airspeed to converge to the optimal velocity without knowing the system dynamics. We also propose a model-predictive method to minimize the energy usage in landing and take-off by optimizing the flight trajectory.
A Retina-Inspired Pathway to Real-Time Motion Prediction inside Image Sensors for Extreme-Edge Intelligence
Authors: Subhradip Chakraborty, Shay Snyder, Md Abdullah-Al Kaiser, Maryam Parsa, Gregory Schwartz, Akhilesh R. Jaiswal
Date: 2025-04-02 00:00:00
Tasks: motion prediction, Prediction

The ability to predict motion in real time is fundamental to many maneuvering activities in animals, particularly those critical for survival, such as attack and escape responses. Given its significance, it is no surprise that motion prediction in animals begins in the retina. Similarly, autonomous systems utilizing computer vision could greatly benefit from the capability to predict motion in real time. Therefore, for computer vision applications, motion prediction should be integrated directly at the camera pixel level. Towards that end, we present a retina-inspired neuromorphic framework capable of performing real-time, energy-efficient MP directly within camera pixels. Our hardware-algorithm framework, implemented using GlobalFoundries 22nm FDSOI technology, integrates key retinal MP compute blocks, including a biphasic filter, spike adder, nonlinear circuit, and a 2D array for multi-directional motion prediction. Additionally, integrating the sensor and MP compute die using a 3D Cu-Cu hybrid bonding approach improves design compactness by minimizing area usage and simplifying routing complexity. Validated on real-world object stimuli, the model delivers efficient, low-latency MP for decision-making scenarios reliant on predictive visual computation, while consuming only 18.56 pJ/MP in our mixed-signal hardware implementation.
R2DN: Scalable Parameterization of Contracting and Lipschitz Recurrent Deep Networks
Authors: Nicholas H. Barbara, Ruigang Wang, Ian R. Manchester
Date: 2025-04-01 00:00:00

This paper presents the Robust Recurrent Deep Network (R2DN), a scalable parameterization of robust recurrent neural networks for machine learning and data-driven control. We construct R2DNs as a feedback interconnection of a linear time-invariant system and a 1-Lipschitz deep feedforward network, and directly parameterize the weights so that our models are stable (contracting) and robust to small input perturbations (Lipschitz) by design. Our parameterization uses a structure similar to the previously-proposed recurrent equilibrium networks (RENs), but without the requirement to iteratively solve an equilibrium layer at each time-step. This speeds up model evaluation and backpropagation on GPUs, and makes it computationally feasible to scale up the network size, batch size, and input sequence length in comparison to RENs. We compare R2DNs to RENs on three representative problems in nonlinear system identification, observer design, and learning-based feedback control and find that training and inference are both up to an order of magnitude faster with similar test set performance, and that training/inference times scale more favorably with respect to model expressivity.
An Adaptive Control Approach to Treatment Selection for Substance Use Disorders
Authors: Eric Pulick, Yonatan Mintz
Date: 2025-04-01 00:00:00

Despite the massive costs and widespread harms of substance use, most individuals with substance use disorders (SUDs) receive no treatment at all. Digital therapeutics platforms are an emerging low-cost and low-barrier means of extending treatment to those who need it. While there is a growing body of research focused on how treatment providers can identify which patients need SUD support (or when they need it), there is very little work that addresses how providers should select treatments that are most appropriate for a given patient. Because SUD treatment involves months or years of voluntary compliance from the patient, treatment adherence is a critical consideration for the treatment provider. In this paper we focus on algorithms that a treatment provider can use to match the burden-level of proposed treatments to the time-varying engagement state of the patient to promote adherence. We propose structured models for a patient's engagement over time and their treatment adherence decisions. Using these models we pose a stochastic control formulation of the treatment-provider's burden selection problem. We propose an adaptive control approach that estimates unknown patient parameters as new data are observed. We show that these estimates are consistent and propose algorithms that use these estimates to make appropriate treatment recommendations.
Long-Range Rendezvous and Docking Maneuver Control of Satellite using Cross-Feedback Sliding Mode Controller
Authors: Vedant Vivek Kini, Dantu Phani Surya, Rakesh Kumar Sahoo, Manoranjan Sinha
Date: 2025-04-01 00:00:00

Satellite rendezvous and docking (RvD) maneuvers are essential for satellite servicing and in-orbit assembly. Traditional approaches often treat translational and rotational motions independently, simplifying control design but potentially leading to inefficiencies in maneuver time and fuel consumption. To address these challenges, a novel cross-feedback sliding mode controller has been proposed, developing an interdependent regulation system for translational and rotational motion. This method decouples the relative translational and rotational motion of chaser satellite with respect to target satellite while incorporating cross-feedback mechanisms to account for their inherent coupling. By incorporating rotational state information into translational control laws and vice versa, the approach ensures coordinated adjustments, enhancing maneuver efficiency. The chaser satellite manages both translational and rotational adjustments to rendezvous and dock with the target satellite. The stability of the cross-feedback sliding mode controller is established within the Lyapunov framework, and simulation results substantiate the effectiveness of this strategy.
Incompressible Optimal Transport and Applications in Fluid Mixing
Authors: Max Emerick, Bassam Bamieh
Date: 2025-04-01 00:00:00

The problem of incompressible fluid mixing arises in numerous engineering applications and has been well-studied over the years, yet many open questions remain. This paper aims to address the question "what do efficient flow fields for mixing look like, and how do they behave?" We approach this question by developing a framework which is inspired by the dynamic and geometric approach to optimal mass transport. Specifically, we formulate the fluid mixing problem as an optimal control problem where the dynamics are given by the continuity equation together with an incompressibility constraint. We show that within this framework, the set of reachable fluid configurations can formally be endowed with the structure of an infinite-dimensional Riemannian manifold, with a metric which is induced by the control effort, and that flow fields which are maximally efficient at mixing correspond to geodesics in this Riemannian space.
LQR based $ω-$stabilization of a heat equation with memory
Authors: Bhargav Pavan Kumar Sistla, Wasim Akram, Debanjana Mitra, Vivek Natarajan
Date: 2025-04-01 00:00:00

We consider a heat equation with memory which is defined on a bounded domain in $\mathbb{R}^d$ and is driven by $m$ control inputs acting on the interior of the domain. Our objective is to numerically construct a state feedback controller for this equation such that, for each initial state, the solution of the closed-loop system decays exponentially to zero with a decay rate larger than a given rate $\omega>0$, i.e. we want to solve the $\omega$-stabilization problem for the heat equation with memory. We first show that the spectrum of the state operator $A$ associated with this equation has an accumulation point at $-\omega_0<0$. Given a $\omega\in(0,\omega_0)$, we show that the $\omega$-stabilization problem for the heat equation with memory is solvable provided certain verifiable conditions on the control operator $B$ associated with this equation hold. We then consider an appropriate LQR problem for the heat equation with memory. For each $n\in\mathbb{N}$, we construct finite-dimensional approximations $A_n$ and $B_n$ of $A$ and $B$, respectively, and then show that by solving a corresponding approximation of the LQR problem a feedback operator $K_n$ can be computed such that all the eigenvalues of $A_n + B_n K_n$ have real part less than $-\omega$. We prove that $K_n$ for $n$ sufficiently large solves the $\omega$-stabilization problem for the heat equation with memory. A crucial and nontrivial step in our proof is establishing the uniform (in $n$) stabilizability of the pair $(A_n+\omega I, B_n)$. We have validated our theoretical results numerically using two examples: an 1D example on a unit interval and a 2D example on a square domain.
MPCritic: A plug-and-play MPC architecture for reinforcement learning
Authors: Nathan P. Lawrence, Thomas Banker, Ali Mesbah
Date: 2025-04-01 00:00:00
Tasks: Model Predictive Control, Reinforcement Learning (RL)

The reinforcement learning (RL) and model predictive control (MPC) communities have developed vast ecosystems of theoretical approaches and computational tools for solving optimal control problems. Given their conceptual similarities but differing strengths, there has been increasing interest in synergizing RL and MPC. However, existing approaches tend to be limited for various reasons, including computational cost of MPC in an RL algorithm and software hurdles towards seamless integration of MPC and RL tools. These challenges often result in the use of "simple" MPC schemes or RL algorithms, neglecting the state-of-the-art in both areas. This paper presents MPCritic, a machine learning-friendly architecture that interfaces seamlessly with MPC tools. MPCritic utilizes the loss landscape defined by a parameterized MPC problem, focusing on "soft" optimization over batched training steps; thereby updating the MPC parameters while avoiding costly minimization and parametric sensitivities. Since the MPC structure is preserved during training, an MPC agent can be readily used for online deployment, where robust constraint satisfaction is paramount. We demonstrate the versatility of MPCritic, in terms of MPC architectures and RL algorithms that it can accommodate, on classic control benchmarks.
Analyzing cell-to-cell heterogeneities and cell configurations in parallel-connected battery modules using physics-based modeling
Authors: Simone Fasolato, Anirudh Allam, Simona Onori, Davide M. Raimondo
Date: 2025-04-01 00:00:00

In parallel-connected cells, cell-to-cell (CtC) heterogeneities can lead to current and thermal gradients that may adversely impact the battery performance and aging. Sources of CtC heterogeneity include manufacturing process tolerances, poor module configurations, and inadequate thermal management. Understanding which CtC heterogeneity sources most significantly impact battery performance is crucial, as it can provide valuable insights. In this study, we use an experimentally validated electrochemical battery model to simulate hundreds of battery configurations, each consisting of four cells in parallel. We conduct a statistical analysis to evaluate the relative importance of key cell-level parameters, interconnection resistance, cell spacing, and location on performance and aging. The analysis reveals that heterogeneities in electrode active material volume fractions primarily impact module capacity, energy, and cell current, leading to substantial thermal gradients. However, to fully capture the output behavior, interconnection resistance, state of charge gradients and the effect of the temperature on parameter values must also be considered. Additionally, module design configurations, particularly cell location, exacerbate thermal gradients, accelerating long-term module degradation. This study also offers insights into optimizing cell arrangement during module design to reduce thermal gradients and enhance overall battery performance and longevity. Simulation results with four cells indicate a reduction of 51.8% in thermal gradients, leading to a 5.2% decrease in long-term energy loss.
Physics-informed machine learning for building performance simulation-A review of a nascent field
Authors: Zixin Jiang, Xuezheng Wang, Han Li, Tianzhen Hong, Fengqi You, Ján Drgoňa, Draguna Vrabie, Bing Dong
Date: 2025-04-01 00:00:00
Tasks: Physics-informed machine learning

Building performance simulation (BPS) is critical for understanding building dynamics and behavior, analyzing performance of the built environment, optimizing energy efficiency, improving demand flexibility, and enhancing building resilience. However, conducting BPS is not trivial. Traditional BPS relies on an accurate building energy model, mostly physics-based, which depends heavily on detailed building information, expert knowledge, and case-by-case model calibrations, thereby significantly limiting their scalability. With the development of sensing technology and increased data availability, there is a growing attention and interest in data-driven BPS. However, purely data-driven models often suffer from limited generalization ability and a lack of physical consistency, resulting in poor performance in real-world applications. To address these limitations, recent studies have started to incorporate physics priors into data-driven models, a methodology called physics-informed machine learning (PIML). PIML is an emerging field with the definitions, methodologies, evaluation criteria, application scenarios, and future directions that remain open. To bridge those gaps, this study systematically reviews the state-of-art PIML for BPS, offering a comprehensive definition of PIML, and comparing it to traditional BPS approaches regarding data requirements, modeling effort, performance and computation cost. We also summarize the commonly used methodologies, validation approaches, application domains, available data sources, open-source packages and testbeds. In addition, this study provides a general guideline for selecting appropriate PIML models based on BPS applications. Finally, this study identifies key challenges and outlines future research directions, providing a solid foundation and valuable insights to advance R&D of PIML in BPS.
Timely Trajectory Reconstruction in Finite Buffer Remote Tracking Systems
Authors: Sunjung Kang, Vishrant Tripathi, Christopher G. Brinton
Date: 2025-04-01 00:00:00
Tasks: Management

Remote tracking systems play a critical role in applications such as IoT, monitoring, surveillance and healthcare. In such systems, maintaining both real-time state awareness (for online decision making) and accurate reconstruction of historical trajectories (for offline post-processing) are essential. While the Age of Information (AoI) metric has been extensively studied as a measure of freshness, it does not capture the accuracy with which past trajectories can be reconstructed. In this work, we investigate reconstruction error as a complementary metric to AoI, addressing the trade-off between timely updates and historical accuracy. Specifically, we consider three policies, each prioritizing different aspects of information management: Keep-Old, Keep-Fresh, and our proposed Inter-arrival-Aware dropping policy. We compare these policies in terms of impact on both AoI and reconstruction error in a remote tracking system with a finite buffer. Through theoretical analysis and numerical simulations of queueing behavior, we demonstrate that while the Keep-Fresh policy minimizes AoI, it does not necessarily minimize reconstruction error. In contrast, our proposed Inter-arrival-Aware dropping policy dynamically adjusts packet retention decisions based on generation times, achieving a balance between AoI and reconstruction error. Our results provide key insights into the design of efficient buffer management policies for resource-constrained IoT networks.
Semi-Data-Driven Model Predictive Control: A Physics-Informed Data-Driven Control Approach
Authors: Sebastian Zieglmeier, Mathias Hudoba de Badyn, Narada D. Warakagoda, Thomas R. Krogstad, Paal Engelstad
Date: 2025-04-01 00:00:00
Tasks: Model Predictive Control

Data-enabled predictive control (DeePC) has emerged as a powerful technique to control complex systems without the need for extensive modeling efforts. However, relying solely on offline collected data trajectories to represent the system dynamics introduces certain drawbacks. Therefore, we present a novel semi-data-driven model predictive control (SD-MPC) framework that combines (limited) model information with DeePC to address a range of these drawbacks, including sensitivity to noisy data, lack of robustness, and a high computational burden. In this work we focus on the performance of DeePC in operating regimes not captured by the offline collected data trajectories and demonstrate how incorporating an underlying parametric model can counteract this issue. SD-MPC exhibits equivalent closed-loop performance as DeePC for deterministic linear time-invariant systems. Simulations demonstrate the general control performance of the proposed SD-MPC for both a linear time-invariant system and a nonlinear system modeled as a linear parameter-varying system. These results provide numerical evidence of the enhanced robustness of SD-MPC over classical DeePC.
Reinforcement learning for robust dynamic metabolic control
Authors: Sebastián Espinel-Ríos, River Walser, Dongda Zhang
Date: 2025-04-01 00:00:00
Tasks: Model Predictive Control, reinforcement-learning, Reinforcement Learning

Dynamic metabolic control can enhance bioprocess flexibility and expand the available optimization degrees of freedom via real-time modulation of metabolic enzyme expression. This allows target metabolic fluxes to be dynamically tuned throughout the process. However, identifying optimal dynamic control policies is challenging due to the presence of potential metabolic burden, cytotoxic effects, and the generally high-dimensional solution space, making exhaustive experimentation impractical. Here, we propose an approach based on reinforcement learning to derive optimal dynamic metabolic control policies by allowing an agent or controller to interact with a surrogate dynamic model $\textit{in silico}$. To incorporate and test robustness, we apply domain randomization, enabling the controller to generalize across system uncertainties. Our approach provides an alternative to conventional model-based control such as model predictive control, which requires differentiating the models with respect to decision variables; an often impractical task when dealing with complex stochastic, nonlinear, stiff, or piecewise-defined dynamics. In contrast, our approach only requires forward integration, making the task computationally much simpler with off-the-shelf solvers. We demonstrate our approach with a case study on the dynamic control of acetyl-CoA carboxylase in $\textit{Escherichia coli}$ for fatty acid biosynthesis. The derived dynamic metabolic control policies outperform static control, achieving up to 40 % higher titers while remaining robust under uncertainty.
Intermodal Network of Autonomous Mobility-on-Demand and Micromobility Systems
Authors: S. J. Abbasi Koumleh, Fabio Paparella
Date: 2025-04-01 00:00:00
Tasks: Autonomous Vehicles

This paper studies models for Autonomous Micromobility-on-Demand (AMoD), a paradigm in which a fleet of autonomous vehicles delivers mobility services on demand in conjunction with micromobility systems. Specifically, we introduce a network flow model to encapsulate the interaction between AMoD and micromobility under an intermodal connection scenario. The primary objective is to analyze the system's behavior, optimizing passenger travel time. Following this theoretical development, we apply these models to the transportation networks of Sioux Falls, enabling a quantifiable evaluation of the reciprocal influences between the two transportation modes. We found that increasing the number of vehicles in any of these two modes of transportation also incentivizes users to use the other. Moreover, increasing the rebalancing capacity of the micromobility system will make the AMoD system need less rebalancing.
Stochastic Model Predictive Control of Charging Energy Hubs with Conformal Prediction
Authors: Diego Fernández-Zapico, Theo Hofman, Mauro Salazar
Date: 2025-04-01 00:00:00
Tasks: Conformal Prediction, energy management, Model Predictive Control, Uncertainty Quantification

This paper presents an online energy management system for an energy hub where electric vehicles are charged combining on-site photovoltaic generation and battery energy storage with the power grid, with the objective to decide on the battery (dis)charging to minimize the costs of operation. To this end, we devise a scenario-based stochastic model predictive control (MPC) scheme that leverages probabilistic 24-hour-ahead forecasts of charging load, solar generation and day-ahead electricity prices to achieve a cost-optimal operation of the energy hub. The probabilistic forecasts leverage conformal prediction providing calibrated distribution-free confidence intervals starting from a machine learning model that generates no uncertainty quantification. We showcase our controller by running it over a 280-day evaluation in a closed-loop simulated environment to compare the observed cost of two scenario-based MPCs with two deterministic alternatives: a version with point forecast and a version with perfect forecast. Our results indicate that, compared to the perfect forecast implementation, our proposed scenario-based MPCs are 11\% more expensive, and 1\% better than their deterministic point-forecast counterpart.