An optimization framework for hot-rolled strip crown control based on model-driven digital twin

A novel distributed-decentralized fixed-time optimal frequency and excitation (Di-De-FTOFE) control framework in a network-preserving power system with nonlinear power flow is proposed in this paper. Firstly, to obtain the solution of the economic dispatch (ED) problem, the proposed control framework contains the distributed fixed-time consensus-based optimal frequency (Di-FTCOF) control to make the Lagrange multiplier of each generation reach consensus in a fixed time and the decentralized fixed-time excitation (De-FTE) control to ensure the power system frequency back to the rated value in a fixed time for satisfying the load-generation balance constraints of ED problem. Secondly, the voltage dynamic also can be maintained around the rated value by the designed De-FTE control which owns good universality to coordinate other optimal frequency control methods. Thirdly, the Di-De-FTOFE control framework is robust to communication failure through the self-adaptive adjustment, which is both analyzed in the theoretical verification and simulation. Finally, the performance of the Di-De-FTOFE control framework is evaluated in simulation with several adverse situations, such as load decrease/increase and communication failure.

Single-stage grid-connected photovoltaic (PV) systems face significant challenges in maintaining voltage stability and meeting accurate power quality (PQ) standards during rapidly fluctuating irradiance, load variations, and grid disturbances. This study presents a Digital Twin (DT) architecture that enables real-time voltage regulation and PQ optimization by digitally replicating the physical PV system for predictive analysis, controller tuning, and operational decision-making. The proposed framework integrates a highly accurate physical model, analytical computations, and adaptive control algorithms driven by data to minimize total harmonic distortion (THD), maintain voltage within allowable limits, and comply with IEEE 1547 and IEC 61727 standards. The DT synchronizes real-time data from voltage and current sensors to capture dynamic system behavior, allowing for pre-deployment testing of voltage regulation strategies, including classical, adaptive, and AI-assisted methods. The architecture incorporates enhanced filtering and stability assessment techniques based on classical control and predictive & nonlinear control techniques, ensuring robustness under weak grid conditions. Simulation and hardware-in-the-loop (HIL) evaluations demonstrate that the DT-assisted control framework reduces THD by more than 40%, maintains voltage within ± 1 %, and improves transient settling times compared to conventional control methods. Future work will explore the integration of DTs with distributed energy resource (DER) coordination, cyberphysical security mechanisms, and self-learning controllers to enable fully autonomous, grid-supportive PV operations in nextgeneration smart grids.

The convergence of Industrial Internet of Things (IIoT) and digital twin technology offers new paradigms for process automation and control. This paper presents an integrated IIoT and digital twin framework for intelligent control of a gas–liquid separation unit with interacting flow, pressure, and differential pressure loops. A comprehensive dynamic model of the three-loop separator process is developed, linearized, and validated. Classical stability analyses using the Routh–Hurwitz criterion and Nyquist plots are employed to ensure stability of the control system. Decentralized multi-loop proportional–integral–derivative (PID) controllers are designed and optimized using the Integral Absolute Error (IAE) performance index. A digital twin of the separator is implemented to run in parallel with the physical process, synchronized via a Kalman filter to real-time sensor data for state estimation and anomaly detection. The digital twin also incorporates structured singular value (μ) analysis to assess robust stability under model uncertainties. The system architecture is realized with low-cost hardware (Arduino Mega 2560, MicroMotion Coriolis flowmeter, pneumatic control valves, DAC104S085 digital-to-analog converter, and ENC28J60 Ethernet module) and software tools (Proteus VSM 8.4 for simulation, VB.Net 2022 version based human–machine interface, and ML.Net 2022 version for predictive analytics). Experimental results demonstrate improved control performance with reduced overshoot and faster settling times, confirming the effectiveness of the IIoT–digital twin integration in handling loop interactions and disturbances. The discussion includes a comparative analysis with conventional control and outlines how advanced strategies such as model predictive control (MPC) can further augment the proposed approach. This work provides a practical pathway for applying IIoT and digital twins to industrial process control, with implications for enhanced autonomy, reliability, and efficiency in oil and gas operations.

Agrivoltaics offer a sustainable solution to the growing competition between food and energy production. However, their adoption is often constrained by the design and operation challenges associated with optimising the complex trade-off between crop yield and photovoltaic (PV) output. Digital twins can mitigate these risks, yet most agricultural digital twins operate as fragmented digital shadows, lacking high-fidelity modelling, advanced simulation, and bidirectional control capabilities. This study presents a comprehensive, end-to-end digital twin framework to address these limitations. The framework integrates a high-resolution 3D orchard model, reconstructed via UAV photogrammetry, with a CesiumJS-based web interface linked to a modular IoT architecture built on Node-RED, Message Queuing Telemetry Transport (MQTT) protocol and InfluxDB for real-time monitoring and control. A PV simulation engine supports the design, simulation and optimisation of agrivoltaic systems. Bidirectional communication was validated through remote actuation of a physical solar tracker, demonstrating integration among the 3D environment, sensor data and control systems to achieve a closed-loop digital twin. Simulation analyses suggested that panel orientation and row spacing exert a dominant influence on crop-level light distribution. Simulation results demonstrated that a 90° azimuth configuration achieved the highest daily energy yield of 53.97 kWh but reduced peak crop-level irradiance to 205 W/m2. In contrast, the baseline 0° configuration offered a balanced output of 40.86 kWh with a peak light availability of 338 W/m2. The validated, interoperable digital twin architecture provides a reference model for the design, simulation, monitoring and control of an agrivoltaic system, reducing investment uncertainty and supporting sustainable food–energy co-production.

A digital twin (DT), with the components of a physics-based model, a data-driven model, and a machine learning (ML) enabled efficient surrogate model, behaves as a virtual twin of the real-world physical process. In terms of Laser Powder Bed Fusion (L-PBF) based additive manufacturing (AM), a DT can predict the current and future states of the melt pool and the resulting defects corresponding to the input laser parameters, evolve itself by assimilating in-situ sensor data, and optimize the laser parameters to mitigate defect formation. In this paper, we present a deep neural operator enabled DT framework for closed-loop feedback control of the L-PBF process. This is accomplished by building a physics-based computational model to accurately represent the melt pool states, an efficient Fourier neural operator (FNO) based surrogate model to approximate the melt pool solution field, followed by a physics-based procedure to extract information from the computed melt pool simulation that can further be correlated to the defect quantities of interest (e.g., surface roughness). An optimization algorithm is then exercised to control laser input and minimize defects. On the other hand, the constructed DT also evolves with the physical twin via offline finetuning and online material calibration. For instance, the probabilistic distribution of laser absorptivity can be updated to match the real-time captured thermal image data. Finally, a probabilistic framework is adopted for uncertainty quantification. The developed DT is envisioned to guide the AM process and facilitate high-quality manufacturing in L-PBF-based metal AM.

Structural vibration control with spatially varied disturbance input using a spatial [formula omitted] method

Medical device regulatory standards are increasingly incorporating computational modelling and simulation to accommodate advanced manufacturing and device personalization. We present a method for robust testing of engineered soft tissue products involving a digital twin paradigm in combination with robotic systems. We developed and validated a digital twin framework for calibrating and controlling robotic-biological systems. A forward dynamics model of the robotic manipulator was developed, calibrated, and validated. After calibration, the accuracy of the digital twin in reproducing the experimental data improved in the time domain for all fourteen tested configurations and improved in frequency domain for nine configurations. We then demonstrated displacement control of a spring in lieu of a soft tissue element in a biological specimen. The simulated experiment matched the physical experiment with 0.09 mm (0.001%) root-mean-square error for a 2.9 mm (5.1%) length change. Finally, we demonstrated kinematic control of a digital twin of the knee through 70-degree passive flexion kinematics. The root-mean-square error was 2.00°, 0.57°, and 1.75° degrees for flexion, adduction, and internal rotations, respectively. The system well controlled novel mechanical elements and generated accurate kinematics in silico for a complex knee model. This calibration method could be applied to other situations where the specimen is poorly represented in the model environment (e.g., human or animal tissues), and the control system could be extended to track internal parameters such as tissue strain (e.g., control knee ligament strain). Further development of this framework can facilitate medical device testing and innovative biomechanics research.

Towards a digital twin framework in additive manufacturing: Machine learning and bayesian optimization for time series process optimization

This paper presents a digital-twin-based model predictive control framework for autonomous process control, demonstrated in a virtual experiment on thermal food processing in a convection oven. In combination with prior work, this approach enables simulation-centered food scientists to deploy physics-based simulation models in live process control environments. The digital twin is realized as a physics-based, data-driven reduced-order model (ROM) that provides faster-than-real-time predictions. The ROM is trained on trajectories from a high-fidelity multiphysics finite-element model of chicken fillets. A central contribution is a model predictive control scheme that overcomes the common fixed-initial-condition limitation of augmented neural ordinary differential equation ROMs: a dedicated sub-optimization step re-synchronizes the surrogate to the measured state of the food item at each control instant, allowing reliable live re-optimization without access to internal ROM states. The controller optimizes oven temperature setpoints to meet target food-quality metrics (core temperature, moisture content, texture) and autonomously accommodates changes to the planned end time during operation. Quantitatively, the ROM achieves relative time-series errors of 0.18–0.49%, and the control algorithm evaluates 501 trajectories of 1800 s real time in a total of 46.6 s on a single core of a processor, demonstrating on-device feasibility without cloud or edge resources. Receding-horizon model predictive control of the remaining setpoints mitigates model–reality mismatch, enforces user-defined food metrics, and sustains closed-loop performance under autonomous operation. • Software-agnostic pipeline to deploy digital-twin surrogates for online control. • Accurate, faster-than-real-time predictions enable autonomous operation. • Online sub-optimization re-synchronizes predictions to measured core temperature. • Level 5 autonomy case beyond the trained time window. • On-device digital twin operation possible without cloud or edge resources.

Digital twins for additive manufacturing (AM) have drawn much research attention recently, thanks to advancements in artificial intelligence and machine learning. Machine learning takes the process and measurement data from the manufacturing process to build data-driven models instead of physics-based descriptive models. The latter are usually hard to obtain for complex AM processes such as laser powder bed fusion. This study proposed a digital twin framework for the laser powder bed fusion AM process control and optimization. The framework is created based on the recently developed advanced point-wise scan control method. It consists of four components: digital twin of process design, digital twin of process control, digital twin of process monitoring, and digital twin of printed part. Their construction is detailed, and potential applications are demonstrated/discussed.

This study presents an artificial intelligence (AI)-enabled framework for adaptive and predictive control in Laser Metal Arc Welding (LMAW) operations. The proposed system integrates machine learning (ML) for defect prediction and reinforcement learning (RL) for dynamic process optimization. Multi-sensor data—including thermal, optical, and acoustic signals—are acquired in real time to train predictive models and guide parameter control. A 50 kW industrial-like LMAW case study is developed to evaluate system performance under variable operating conditions. Results demonstrate that the AI-optimized control strategy reduces energy consumption by 22 %, decreases defect occurrence by 30 %, and lowers CO₂ emissions by approximately 18 % compared with baseline operation. The predictive model achieves an accuracy of 96 % in identifying weld defects, while RL-based control stabilizes the arc temperature within ±3 % of the target range (2500–3100 °C). The findings confirm that AI-driven process intelligence can significantly enhance welding efficiency, quality, and sustainability in alignment with Industry 4.0 objectives

In the development of resource-efficient and user-centered power tools, it is essential to identify a set of operating points corresponding to specific work processes, as performed under real-world field conditions. For power tools like chainsaws, the operating point is influenced by the applied load, which results from the interaction between the machine, the work piece, and — most critically — the user’s behavior. To effectively support the development through a digital twin (DT) prototype, it is vital to incorporate the human factor into the digital model. Current research lacks straightforward methods for modeling user behavior. Most existing literature focuses either on the DT of the product itself or on complex, large-scale approaches to modeling human behavior. This article explores the integration of a human controller (HC) framework into the DT environment for power tools. The objective is to generate added value in the early stages of the product life cycle with minimal effort, while also enabling a more detailed representation of a human twin. As a case study, the use of a chainsaw during the processing of a felled tree is examined. Relevant human behavioral characteristics are identified through empirical measurements. The functionality of the integrated HC is then verified within the DT prototype.

Wireless Power Transfer (WPT) for Electric Vehicles (EVs) offers a promising solution for convenient and efficient charging. However, misalignments, sensor noise, and parameter variability can significantly degrade Power Transfer Efficiency (PTE). This study proposes a novel unified artificial intelligence (AI)-driven optimization and control framework that integrates Genetic Algorithm (GA)-based static optimization, Artificial Neural Network (ANN) surrogate modeling, and Reinforcement Learning (RL) dynamic control using the Proximal Policy Optimization (PPO) algorithm. This unified design bridges the gap between previous static-only optimization methods and dynamic adaptive controllers, enabling both peak efficiency and verified robustness within a single digital twin simulation environment. A high-fidelity MATLAB/Simulink model of the WPT system was developed and validated using an ANN surrogate model (Test MSE: 7.87×10−13). The GA-optimized configuration achieved a peak PTE of 85.47%, representing a 2.11 percentage-point improvement over the baseline. The RL controller, based on PPO, maintained a mean efficiency of approximately 80% under unseen trajectories, ±10% hardware parameter variations, and Gaussian sensor noise (σ=0.56%), demonstrating superior adaptability. Comparative analysis with state-of-the-art studies confirms that the proposed approach not only matches or exceeds the reported efficiency gains, but also uniquely integrates robustness validation and generalization testing. The results suggest that combining offline GA optimization with online RL adaptation provides a scalable, real-time control strategy for practical WPT deployments.

We consider the problem of optimizing the cost of procuring electricity for a large collection of homes managed by a load serving entity, by pre-cooling or pre-heating the thermal inertial loads in the homes to avoid procuring power during periods of peak electricity pricing. We would like to accomplish this objective in a completely privacy-preserving and model-free manner, that is, without direct access to the state variables (temperatures or power consumption) or the dynamical models (thermal characteristics) of individual homes, while guaranteeing personal comfort constraints of the consumers. We propose a two-stage optimization and control framework to address this problem. In the first stage, we use a long short-term memory (LSTM) network to predict hourly electricity prices, based on historical pricing data and weather forecasts. Given the hourly price forecast and thermal models of the homes, the problem of designing an optimal power consumption trajectory that minimizes the total electricity procurement cost for the collection of thermal loads can be formulated as a large-scale integer program (with millions of variables) due to the on-off cyclical dynamics of such loads. We provide a simple heuristic relaxation to make this large-scale optimization problem model-free and computationally tractable. In the second stage, we translate the results of this optimization problem into distributed open-loop control laws that can be implemented at individual homes without measuring or estimating their state variables, while simultaneously ensuring consumer comfort constraints. We demonstrate the performance of this approach on a large-scale test case comprising of 500 homes in the Houston area and benchmark its performance against a direct model-based optimization and control solution.

In this paper, we study the congestion control problem in TCP satellite networks, and present a new design for TCP network congestion control based on a nonlinear control and optimization framework. The proposed TCP/AQM control framework consists of two levels: the high-level generates the desired queue size for routers in the network by solving a network utility maximization problem using a distributed optimization scheme; the low-level provides a local nonlinear feedback control for the probability of packet mark/drop based on a typical fluid-flow model for TCP dynamical behaviors. The nonlinear control design is done using a backstepping method and its stability is proven by the Lyapunov direct method. Simulation results are included to show the effectiveness of the proposed new design.