arXiv subjects

Satadru Dey

Publications and source records attributed to Satadru Dey.

17 recordsLinked to original sources

arXiv2026

Robust Linear Quadratic Optimal Control of Cementitious Material Extrusion

Extrusion-based 3D printing of cementitious materials enables fabrication of complex structures, however it is highly sensitive to disturbances, material property variations, and process uncertainties that decrease flow stability and dimensional fidelity. To address these challenges, this study proposes a robust linear quadratic optimal control framework for regulating material extrusion in cementitious direct ink writing systems. The printer is modeled using two coupled subsystems: an actuation system representing nozzle flow dynamics and a printing system describing the printed strand flow on the build plate. A hybrid control architecture combining sliding mode control for disturbance rejection with linear quadratic optimal feedback for energy-efficient tracking is developed to ensure robustness and optimality. In simulation case studies, the control architecture guarantees acceptable convergence of nozzle and strand flow tracking errors under bounded disturbances.

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arXiv2026

Real-Time Regulation of Direct Ink Writing Using Model Reference Adaptive Control

Direct Ink Writing (DIW) has gained attention for its potential to reduce printing time and material waste. However, maintaining precise geometry and consistent print quality remains challenging under dynamically varying operating conditions. This paper presents a control-focused approach using a model reference adaptive control (MRAC) strategy based on a reduced-order model (ROM) of extrusion-based 3D printing for a candidate cementitious material system. The proposed controller actively compensates for uncertainties and disturbances by adjusting process parameters in real time, with the objective of minimizing reference-tracking errors. Stability and convergence are rigorously verified via Lyapunov analysis, demonstrating that tracking errors asymptotically approach zero. Performance evaluation under realistic simulation scenarios confirms the effectiveness of the adaptive control framework in maintaining accurate and robust extrusion behavior.

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arXiv2025

Physics-Informed Dynamical Modeling of Extrusion-Based 3D Printing Processes

The trade-off between model fidelity and computational cost remains a central challenge in the computational modeling of extrusion-based 3D printing, particularly for real time optimization and control. Although high fidelity simulations have advanced considerably for offline analysis, dynamical modeling tailored for online, control-oriented applications is still significantly underdeveloped. In this study, we propose a reduced order dynamical flow model that captures the transient behavior of extrusion-based 3D printing. The model is grounded in physics-based principles derived from the Navier Stokes equations and further simplified through spatial averaging and input dependent parameterization. To assess its performance, the model is identified via a nonlinear least squares approach using Computational Fluid Dynamics (CFD) simulation data spanning a range of printing conditions and subsequently validated across multiple combinations of training and testing scenarios. The results demonstrate strong agreement with the CFD data within the nozzle, the nozzle substrate gap, and the deposited layer regions. Overall, the proposed reduced order model successfully captures the dominant flow dynamics of the process while maintaining a level of simplicity compatible with real time control and optimization.

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arXiv2025

A Cyber-Physical Systems Framework for Tracking Post Thermal-Runaway Temperature and Smoke Dynamics in Underground Mines

Underground mining operations are actively exploring the use of large-format lithium-ion batteries (LIBs) to power their equipment. LIBs have high energy density, long cycle life, and favorable safety record. They also have low noise, heat, and emission footprints. This fosters a conducive workplace environment for underground mining personnel. However, many occurrences of LIB failure have resulted in dangerous situations in underground mines. The combustion products, including toxic emissions, can rapidly travel throughout the mine using the ventilation network. Therefore, it is critical to monitor the temperature and smoke concentration underground at all times to ensure the safety of the miners. High-fidelity models can be developed for specific scenarios of LIB failure, but are computationally prohibitive for large underground mine volumes, complex geometries, and long duration combustion events. To mitigate computation-related issues associated with high-fidelity models, we developed cyber-physical systems (CPS) models to examine temperature and smoke dynamics. The mine supervisory control center, acting as the cyber framework, operates in conjunction with the physical underground mine. The CPS models, trained on high-fidelity computational fluid dynamics (CFD) model data sets, present an exceptional estimate of the evolution of temperature and smoke concentration in the underground mine tunnel. Once implemented, the research results can help mine operators make informed decisions during emergencies.

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arXiv2025

Dynamical Modeling of Temperature and Smoke Evolution in a Thermal-Runaway Event of a Large-Format Lithium-ion Battery in a Mine Tunnel

Large-format lithium-ion batteries (LIBs) provide effective energy storage solutions for high-power equipment used in underground mining operations. They have high Columbic efficiency and minimal heat and emission footprints. However, improper use of LIBs, accidents, or other factors may increase the probability of thermal runaway (TR), a rapid combustion reaction that discharges toxic and flammable substances. Several such incidents have been documented in mines. Since repeatable TR experiments to uncover the transient-state propagation of TR are expensive and hazardous, high-fidelity models are usually developed to mimic the impact of these events. They are resource-intensive and are impractical to develop for many scenarios that could be observed in a mine. Therefore, dynamic models within a reduced-order framework were constructed to represent the transient-state combustion event. Reduced order models (ROMs) reasonably replicate trends in temperature and smoke, showing strong alignment with the ground-truth dataset.

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arXiv2025

Secure Control of Connected and Autonomous Electrified Vehicles Under Adversarial Cyber-Attacks

Connected and Autonomous Electrified Vehicles (CAEV) is the solution to the future smart mobility having benefits of efficient traffic flow and cleaner environmental impact. Although CAEV has advantages they are still susceptible to adversarial cyber attacks due to their autonomous electric operation and the involved connectivity. To alleviate this issue, we propose a secure control architecture of CAEV. Particularly, we design an additional control input using Reinforcement Learning (RL) to be applied to the vehicle powertrain along with the input commanded by the battery. We present simulation case studies to demonstrate the potential of the proposed approach in keeping the CAEV platoon operating safely without collisions by curbing the effect of adversarial attacks.

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arXiv2025

A Physics-Informed Reinforcement Learning Approach for Degradation-Aware Long-Term Charging Optimization in Batteries

Batteries degrade with usage and continuous cycling. This aging is typically reflected through the resistance growth and the capacity fade of battery cells. Over the years, various charging methods have been presented in the literature that proposed current profiles in order to enable optimal, fast, and/or health-conscious charging. However, very few works have attempted to make the ubiquitous Constant Current Constant Voltage (CCCV) charging protocol adaptive to the changing battery health as it cycles. This work aims to address this gap and proposes a framework that optimizes the constant current part of the CCCV protocol adapting to long-term battery degradation. Specifically, a physics-informed Reinforcement Learning (RL) approach has been used that not only estimates a key battery degradation mechanism, namely, Loss of Active Material (LAM), but also adjusts the current magnitude of CCCV as a result of this particular degradation. The proposed framework has been implemented by combining PyBamm, an open-source battery modeling tool, and Stable-baselines where the RL agent was trained using a Proximal Policy Optimization (PPO) network. Simulation results show the potential of the proposed framework for enhancing the widely used CCCV protocol by embedding physics information in RL algorithm. A comparative study of this proposed agent has also been discussed with 2 other charging protocols generated by a non-physics-based RL agent and a constant CCCV for all the cycles.

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arXiv2025

Real-Time Thermal State Estimation and Forecasting in Laser Powder Bed Fusion

Laser Powder Bed Fusion (L-PBF) is a widely adopted additive manufacturing process for fabricating complex metallic parts layer by layer. Effective thermal management is essential to ensure part quality and structural integrity, as thermal gradients and residual stresses can lead to defects such as warping and cracking. However, existing experimental or computational techniques lack the ability to forecast future temperature distributions in real time, an essential capability for proactive process control. This paper presents a real-time thermal state forecasting framework for L-PBF, based on a physics-informed reduced-order thermal model integrated with a Kalman filtering scheme. The proposed approach efficiently captures inter-layer heat transfer dynamics and enables accurate tracking and forecasting of spatial and temporal temperature evolution. Validation across multiple part geometries using measured data demonstrates that the method reliably estimates and forecasts peak temperatures and cooling trends. By enabling predictive thermal control, this framework offers a practical and computationally efficient solution for thermal management in L-PBF, paving the way toward closed-loop control in L-PBF.

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arXiv2025

Model Predictive Control-Based Optimal Energy Management of Autonomous Electric Vehicles Under Cold Temperatures

In autonomous electric vehicles (AEVs), battery energy must be judiciously allocated to satisfy primary propulsion demands and secondary auxiliary demands, particularly the Heating, Ventilation, and Air Conditioning (HVAC) system. This becomes especially critical when the battery is in a low state of charge under cold ambient conditions, and cabin heating and battery preconditioning (prior to actual charging) can consume a significant percentage of available energy, directly impacting the driving range. In such cases, one usually prioritizes propulsion or applies heuristic rules for thermal management, often resulting in suboptimal energy utilization. There is a pressing need for a principled approach that can dynamically allocate battery power in a way that balances thermal comfort, battery health and preconditioning, along with range preservation. This paper attempts to address this issue using real-time Model Predictive Control to optimize the power consumption between the propulsion, HVAC, and battery temperature preparation so that it can be charged immediately once the destination is reached.

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arXiv2025

Exploring Adversarial Threat Models in Cyber Physical Battery Systems

Technological advancements like the Internet of Things (IoT) have facilitated data exchange across various platforms. This data exchange across various platforms has transformed the traditional battery system into a cyber physical system. Such connectivity makes modern cyber physical battery systems vulnerable to cyber threats where a cyber attacker can manipulate sensing and actuation signals to bring the battery system into an unsafe operating condition. Hence, it is essential to build resilience in modern cyber physical battery systems (CPBS) under cyber attacks. The first step of building such resilience is to analyze potential adversarial behavior, that is, how the adversaries can inject attacks into the battery systems. However, it has been found that in this under-explored area of battery cyber physical security, such an adversarial threat model has not been studied in a systematic manner. In this study, we address this gap and explore adversarial attack generation policies based on optimal control framework. The framework is developed by performing theoretical analysis, which is subsequently supported by evaluation with experimental data generated from a commercial battery cell.

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arXiv2023

On Distinguishability of Anomalies as Physical Faults or Actuation Cyberattacks

Increased automation has created an impetus to integrate infrastructure with wide-spread connectivity in order to improve efficiency, sustainability, autonomy, and security. Nonetheless, this reliance on connectivity and the inevitability of complexity in this system increases the vulnerabilities to physical faults or degradation and external cyber-threats. However, strategies to counteract faults and cyberattacks would be widely different and thus it is vital to not only detect but also to identify the nature of the anomaly that is present in these systems. In this work, we propose a mathematical framework to distinguish between physical faults and cyberattack using a sliding mode based unknown input observer. Finally, we present simulation case studies to distinguish between physical faults and cyberattacks using the proposed Distinguishability metric and criterion. The simulation results show that the proposed framework successfully distinguishes between faults and cyberattacks.

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arXiv2023

An Input-to-State Safety Approach Towards Safe Control of a Class of Parabolic PDEs Under Disturbances

Distributed Parameter Systems (DPSs), modelled by partial differential equations (PDEs), are increasingly vulnerable to disturbances arising from various sources. Although detection of disturbances in PDE systems have received considerable attention in existing literature, safety control of PDEs under disturbances remains significantly under-explored. In this context, we explore a practical input-to-state safety (pISSf) based control design approach for a class of DPSs modelled by linear Parabolic PDEs. Specifically, we develop a control design framework for this class of system with both safety and stability guarantees based on control Lyapunov functional and control barrier functional. To illustrate our methodology, we apply our strategy to design a thermal control system for battery modules under disturbance. Several simulation studies are done to show the efficacy of our method.

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arXiv2021

Thermal Fault Detection and Localization Framework for Large Format Batteries

Safety against thermal failures is crucial in battery systems. Real-time thermal diagnostics can be a key enabler of such safer batteries. Thermal fault diagnostics in large format pouch or prismatic cells pose additional challenges compared to cylindrical cells. These challenges arise from the fact that the temperature distribution in large format cells is at least two-dimensional in nature (along length and breadth) while such distribution can be reasonably approximated in one dimension (along radial direction) in cylindrical cells. This difference makes the placement of temperature sensor(s) non-trivial and the design of detection algorithm challenging. In this work, we address these issues by proposing a framework that (i) optimizes the sensor locations to improve detectability and isolability of thermal faults, and (ii) designs a filtering scheme for fault detection and localization based on a two-dimensional thermal model. The proposed framework is illustrated by experimental and simulation studies on a commercial battery cell.

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arXiv2021

Security of Distributed Parameter Cyber-Physical Systems: Cyber-Attack Detection in Linear Parabolic PDEs

Security of Distributed Parameter Cyber-Physical Systems (DPCPSs) is of critical importance in the face of cyber-attack threats. Although security aspects of Cyber-Physical Systems (CPSs) modelled by Ordinary differential Equations (ODEs) have been extensively explored during the past decade, security of DPCPSs has not received its due attention despite its safety-critical nature. In this work, we explore the security aspects of DPCPSs from a system theoretic viewpoint. Specifically, we focus on DPCPSs modelled by linear parabolic Partial Differential Equations (PDEs) subject to cyber-attacks in actuation channel. First, we explore the detectability of such attacks and derive conditions for stealthy attacks. Next, we develop a design framework for cyber-attack detection algorithms based on output injection observers. Such attack detection algorithms explicitly consider stability, robustness and attack sensitivity in their design. Finally, theoretical analysis and simulation studies are performed to illustrate the effectiveness of the proposed approach.

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arXiv2021

Cylindrical Battery Fault Detection under Extreme Fast Charging: A Physics-based Learning Approach

High power operation in extreme fast charging significantly increases the risk of internal faults in Electric Vehicle batteries which can lead to accelerated battery failure. Early detection of these faults is crucial for battery safety and widespread deployment of fast charging. In this setting, we propose a real-time {detection} framework for battery voltage and thermal faults. A major challenge in battery fault detection arises from the effect of uncertainties originating from sensor inaccuracies, nominal aging, or unmodelled dynamics. Inspired by physics-based learning, we explore a detection paradigm that combines physics-based models, model-based detection observers, and data-driven learning techniques to address this challenge. Specifically, we construct the {detection} observers based on an experimentally identified electrochemical-thermal model, and subsequently design the observer tuning parameters following Lyapunov's stability theory. Furthermore, we utilize Gaussian Process Regression technique to learn the model and measurement uncertainties which in turn aid the {detection} observers in distinguishing faults and uncertainties. Such uncertainty learning essentially helps suppressing their effects, potentially enabling early detection of faults. We perform simulation and experimental case studies on the proposed fault {detection} scheme verifying the potential of physics-based learning in early detection of battery faults.

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arXiv2021

Cyber-Attack Detection in Socio-Technical Transportation Systems Exploiting Redundancies Between Physical and Social Data

Cyber-physical-social connectivity is a key element in Intelligent Transportation Systems (ITSs) due to the ever-increasing interaction between human users and technological systems. Such connectivity translates the ITSs into dynamical systems of socio-technical nature. Exploiting this socio-technical feature to our advantage, we propose a cyber-attack detection scheme for ITSs that focuses on cyber-attacks on freeway traffic infrastructure. The proposed scheme combines two parallel macroscopic traffic model-based Partial Differential Equation (PDE) filters whose output residuals are compared to make decision on attack occurrences. One of the filters utilizes physical (vehicle/infrastructure) sensor data as feedback whereas the other utilizes social data from human users' mobile devices as feedback. The Social Data-based Filter is aided by a fake data isolator and a social signal processor that translates the social information into usable feedback signals. Mathematical convergence properties are analyzed for the filters using Lyapunov's stability theory. Lastly, we validate our proposed scheme by presenting simulation results.

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arXiv2021

Route Choice-based Socio-Technical Macroscopic Traffic Model

Human route choice is undeniably one of the key contributing factors towards traffic dynamics. However, most existing macroscopic traffic models are typically concerned with driving behavior and do not incorporate human route choice behavior models in their formulation. In this paper, we propose a socio-technical macroscopic traffic model that characterizes the traffic states using human route choice attributes. Essentially, such model provides a framework for capturing the Cyber-Physical-Social coupling in smart transportation systems. To derive this model, we first use Cumulative Prospect Theory (CPT) to model the human passengers' route choice under bounded rationality. These choices are assumed to be influenced by traffic alerts and other incomplete traffic information. Next, we assume that the vehicles are operating under a non-cooperative cruise control scenario. Accordingly, human route choice segregates the traffic into multiple classes where each class corresponds to a specific route between an origin-destination pair. Thereafter, we derive a Mean Field Game (MFG) limit of this non-cooperative game to obtain a macroscopic model which embeds the human route choice attribute. Finally, we analyze the mathematical characteristics of the proposed model and present simulation studies to illustrate the model behavior.

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