Industrial control systems (ICS) are increasingly targeted by cyberattacks that manipulate physical processes while evading data-driven detectors trained on raw time-series data. This paper extracts 34–41 control-theoretic features, including t...
Sparse ergodic control provides a natural framework for long-run stochastic decision-making under resource constraints. Existing formulations, however, are typically restricted to control-affine systems with control-independent diffusion. When the di...
This study addresses the trajectory tracking control problem for an underactuated hovercraft subject to additive bias and multiplicative loss-of-effectiveness thruster faults under environmental disturbances. In these systems, actuator degradation st...
The global shift toward clean production, like using renewable energy, has significantly decreased the use of synchronous machines (SMs), which help maintain stability and control, causing serious frequency stability issues in power systems with low...
The rapid integration of photovoltaic (PV) generation into modern power networks introduces significant operational challenges, including intermittent power production, uneven charge distribution, and reduced system reliability in multi-battery energ...
Traditional parameter estimation-based trajectory prediction algorithms for Reentry Glide Vehicles (RGVs) typically suffer from limitations, as they neglect the influence of state variables and rely heavily on high-dimensional motion information. To...
As modern systems become more complex, their control strategy no longer relies only on measurement data from probes; it also requires information from mathematical models for non-measurable places. On the other hand, those mathematical models can lea...
The increasing penetration of renewable energy sources in inverter-dominated microgrids introduces significant challenges for maintaining voltage and frequency stability under weak-grid and dynamically varying operating conditions. Conventional inver...
This paper proposes a nonlinear Model Predictive Control (MPC) method based on Physics-Informed Neural Networks (PINNs), aimed at enhancing the trajectory tracking performance of Automated Guided Vehicles (AGVs) in complex dynamic environments. Tradi...
This work has two objectives. Firstly, it describes a novel physics-informed hybrid neural network (PIHNN) model based on the long short-term memory (LSTM) neural network. The presented model structure combines the first-principle process description...
With the continuous increase in renewable energy penetration and the expansion of power grid scale, the computational complexity and solution time of traditional voltage-reactive power (V-Q) control solution methods have grown significantly, making i...
Managed pressure drilling (MPD) is the core technology for developing formations with high pressure and narrow density windows. It precisely maintains the bottomhole pressure (BHP) within the safe operating window defined by formation pore pressure a...
Wavelength stability of distributed feedback (DFB) semiconductor lasers in dense wavelength division multiplexing (DWDM) systems hinges on sub-millikelvin temperature regulation, a task complicated by the nonlinear, multi-node dynamics of the thermoe...
Physics-informed anomaly detectors for industrial control systems are usually validated on a single synthetic or testbed dataset, which can yield optimistic estimates that do not transfer to deployment-like conditions. This validation-to-deployment d...
Accurate representation of the pressure drop–flow rate (Δp–q) relationship of nozzle-type inflow control devices (ICDs) is critical for reliable reservoir–wellbore coupled simulation. Conventional ICD models in reservoir simul...
Integrating electric vehicles (EVs) has become integral to modern power grids to enhance grid stability and support green energy transportation solutions. EVs emerged as a promising energy solution that introduces a significant challenge to the unpre...
This work proposes a novel method for the real-time identification and nonlinear control of a permanent-magnet synchronous motor (PMSM) based on a Physics-Informed Neural Network (PINN) and the exact feedback linearization approach. The proposed appr...
Carbon capture and storage (CCS) is essential for achieving net-zero emissions, yet amine-based capture systems face significant challenges including high energy penalties (20–30% of power plant output) and operational costs ($50–120/tonn...
False data injection attacks (FDIAs) pose a serious threat to FACTS-assisted wide-area damping controllers because they corrupt remote measurements, degrade inter-area oscillation damping, and may evade conventional detection. Data-driven detectors t...
Active voltage control (AVC) in active distribution networks coordinates the reactive power outputs of distributed inverters to maintain bus voltages within secure limits. Although multi-agent reinforcement learning (MARL) shows promise for AVC, curr...
This paper proposes a model predictive control (MPC) approach for ducted fan aerial robots using physics-informed machine learning (ML), where the task is to fully exploit the capabilities of the predictive control design with an accurate dynamic mod...
False data injection attacks (FDIAs) targeting AC state estimation pose significant challenges, especially when only power measurements are available, and voltage measurements are absent. Current machine learning-based approaches struggle to effectiv...
Control in multi-agent decision-making systems is an important issue with a wide variety of existing approaches. In this work, we offer a new comprehensive framework for distributed control. The main contributions of this paper are summarized as foll...
Fuel-cell flying vehicles suffer from limited endurance, while ammonia, decomposed onboard to supply hydrogen, offers a carbon-free, high-density solution to extend flight missions. However, the system’s performance is governed by a multi-scale...
Controlling nonlinear dynamics is a long-standing problem in engineering. Harnessing known physical information to accelerate or constrain stochastic learning pursues a new paradigm of scientific machine learning. By linearizing nonlinear systems, tr...
Physics-informed anomaly detection for industrial control systems (ICSs) is usually validated on synthetic data. How much of that performance survives on real hardware-in-the-loop (HIL) data is an open question. We re-implement PhySec-Edge, a hybrid...
This study evaluates a longitudinal control method integrating a physics-informed neural network (PINN), nonlinear dynamic inversion (NDI), and radial basis function (RBF) compensation using an open-source F-16 dynamics model as a numerical benchmark...
Deep-space asteroid exploration is of significant importance for both deep-space exploration and scientific research. However, the complex gravitational fields near asteroids pose challenges for traditional model-based orbit control strategies. While...
Data-driven aeration optimization is an effective approach for reducing energy consumption in wastewater treatment plants (WWTPs). However, in information-limited scenarios, newly established or emerging-market WWTPs often lack historical labels for...
This editorial synthesizes the key findings from 17 original research articles featured in the Special Issue on “Intelligent and Integrated Approaches for Efficient Oil and Gas Development.” The collection demonstrates a paradigm shift fr...
Liquid storage tanks are widely used in sectors such as water treatment, oil and gas, food processing, and chemical manufacturing. Knowing the exact amount of liquid in a tank is essential for ensuring safety, preventing spills, and optimizing proces...
This paper proposes a physics-informed hybrid digital CO2 emission-control technology for maritime transport, designed for adaptive ship speed optimization along a predefined geographical route between two ports, discretized into quasi-stationary seg...
Power electronic converters are being pushed toward higher power density and switching frequency, turning both design and operation into multi-objective, multi-physics optimization problems. While analytical rules and gradient-based methods remain es...
Fractional optimal control problems provide an effective mathematical framework for modeling dynamical systems with memory, hereditary behavior, and anomalous diffusion effects. However, the nonlocal nature of Caputo fractional operators and the redu...
Indirect methods based on Pontryagin’s Maximum Principle (PMP) offer theoretical rigor for nonlinear optimal control but suffer from extreme sensitivity to costate initialization. Physics-Informed Neural Networks (PINNs) provide a promising dat...
Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for modeling and controlling complex energy systems by embedding physical laws into deep learning architectures. This review paper highlights the application of PINNs in grid-co...
Broadband sound absorption has long been a concern in noise control engineering, but the inverse design of multi-parallel microperforated panels (MPPs) for broadband sound absorption remains challenging. To address this issue, we propose a deep learn...
The dynamic nature of quadrotor flight introduces significant uncertainty in system parameters, such as thrust and drag factors. Consequently, operators grapple with escalating challenges in implementing real-time control actions. This study presents...
This paper presents the PIML-agent, a physics-informed multi-agent strategy for transient voltage support in ultra-high-voltage direct-current (UHVDC) receiving-end systems. A stress-risk signal derived from a reduced-order receiving-end model is fus...
Traditional control of robotic systems relies on the availability of an exact model, which assumes complete knowledge of the robot’s parameters and all dynamic effects. However, this idealized scenario rarely holds in practice, as real-world in...
With the increasing integration of renewable energy sources (RESs) into power systems, batteries are playing a critical role in ensuring grid reliability and flexibility. Among them, vanadium redox flow batteries (VRFBs) have emerged as a promising s...
In this work, we introduce Pontryagin Neural Networks (PoNNs) and employ them to learn the optimal control actions for unconstrained and constrained optimal intercept problems. PoNNs represent a particular family of Physics-Informed Neural Networks (...
This paper presents a physics-informed reinforcement learning framework that embeds thermodynamic constraints directly into the policy network of a continuous control agent for HVAC optimization. We introduce a Thermodynamically-Constrained Deep Dete...
This paper demonstrates the utilization of Pontryagin Neural Networks (PoNNs) to acquire control strategies for achieving fuel-optimal trajectories. PoNNs, a subtype of Physics-Informed Neural Networks (PINNs), are tailored for solving optimal contro...
High-precision thermal regulation in semiconductor process equipment is critical for product quality, yet it is challenged by actuator transport delays, limited actuator bandwidth due to hardware dynamics, and broadband inlet disturbances in temperat...
Controlling nonlinear dynamics arises in various engineering fields. We present efforts to model the forced van der Pol system control using physics-informed neural networks (PINN) compared to benchmark methods, including idealized nonlinear feedforw...
The continuous prediction and uncertainty quantification of unconfined compressive strength (UCS) and the fracture-related index of anisotropy (FRIA) are essential for optimizing drilling operations and hydraulic fracturing design in shale gas develo...
With more renewables being integrated into distribution grids, the problem of voltage fluctuation has become prominent. Effective Volt/VAR regulation is a commonly used method to ensure the safe operation of distribution networks. Model-based approac...
Physical infrastructure failure induces topological phase transitions in financial networks that existing systemic risk models cannot detect, attribute, or govern. This study introduces CASCADEnt (Cascading Systemic-Entropy-Detecting Network), a phys...
The growing penetration of photovoltaic (PV) generation in multi-energy microgrids has amplified the challenges of maintaining real-time operational efficiency, reliability, and safety under conditions of renewable variability and forecast uncertaint...
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