Real-time Closed-loop Control Research Articles

Advancing Offshore Renewable Energy: Integrative Approaches in Floating Offshore Wind Turbine-Oscillating Water Column Systems Using Artificial Intelligence-Driven Regressive Modeling and Proportional-Integral-Derivative Control

This research investigates the integration of Floating Offshore Wind Turbines (FOWTs) with Oscillating Water Columns (OWCs) to enhance sustainable energy generation, focusing on addressing dynamic complexities and uncertainties inherent in such systems. The novelty of this study lies in its dual approach, which integrates regressive modeling with an aero-hydro-elasto-servo-mooring coupled system with a deep data-driven network and implements a proportional-integral-derivative (PID) control mechanism to improve system stability. By employing Artificial Neural Networks (ANNs), the study circumvents the challenges of real-time closed-loop control on FOWT structures using the OpenFAST simulation tool. Data-driven models, trained on OpenFAST datasets, facilitate real-time predictive behavior analysis and decision-making. Advanced computational learning techniques, particularly ANNs, accurately replicate the dynamics of FOWT-OWC numerical models. An intelligent PID control mechanism is subsequently applied to mitigate structural vibrations, ensuring effective control. A comparative analysis with traditional barge-based FOWT systems underscores the enhanced modeling and control methodologies’ effectiveness. In this sense, the experimental results demonstrate substantial reductions in the mean oscillation amplitude, with reductions from 5% to 35% observed across various scenarios. Specifically, at a wave period from 20 s and a wind speed of 5 m/s, the fore-aft displacement was reduced by 35%, exemplifying the PID control system’s robustness and efficacy under diverse conditions. This study highlights the potential of ANN-driven modeling as an alternative to managing the complex non-linear dynamics of NREL 5 MW FOWT models and underscores the significant improvements in system stability through tailored PID gain scheduling across various operational scenarios.

Contact-force-based closed-loop control of shell structure additive manufacturing with continuous-fiber-reinforced polymer composites

Additive manufacturing (AM) is an automated process for fabricating complex structures, especially for continuous fiber-reinforced polymer (CFRP) composites with exceptional mechanical performance and reduced weight. The extrusion-based CFRP-AM process has been widely adopted but faces challenges with insufficient impregnation/adhesion and lack of geometrical accuracy. Existing research efforts exploit various mechatronics approaches and printing parameter adjustment techniques to enhance CFRP quality. Nevertheless, most of them are static or slow-varying; thus, they cannot dynamically adjust to local geometrical features and faults, which are particularly representative of the CFRP-AM process in high-curvature regions. To enable dynamic and rapid adjustment, this study exploits contact forces as major real-time feedback signals to dynamically control the feed velocity, layer height, and fiber extrusion rate. To create a continuous feedback control environment for dynamic adjustment, the study also proposes a novel helical CFRP-AM printing trajectory generation method for closed shell structures, such that the helical angles indirectly adjust the layer height of the parts. A material extrusion CFRP-AM platform combining a co-extrusion nozzle, an industrial robot, a six-degree-of-freedom load cell, and a real-time closed-loop control system is built to verify the proposed control framework. Three representative case studies are introduced to show the enhancements of the closed-loop CFRP-AM process, particularly in layer height adjustment, high-speed printing, and large curvature regions.

Design of real time closed loop control in hybrid energy system using cascaded converters: a dynamic solution for high power applications

ABSTRACT This paper presents a Hybrid Renewable Energy system combining multiple sources of renewable energy, such as PV, Wind, and energy storage devices, to provide a reliable and sustainable power supply. Cascaded converters are used to efficiently transform and manage the energy from these sources. To achieve optimal performance and reliable operation, a real-time closed-loop control algorithm is designed and implemented for the cascaded converter. The control algorithm facilitates the smooth integration of energy from various renewable sources, optimizing the utilization of available resources and ensuring a consistent power supply. The algorithm maximizes the energy conversion efficiency of the cascaded converter, reducing energy losses and minimizing waste. It also optimizes the integration of renewable sources, regulates output parameters, and ensures efficient energy conversion.

A Novel and Self-Calibrating Weighing Sensor with Intelligent Peristaltic Pump Control for Real-Time Closed-Loop Infusion Monitoring in IoT-Enabled Sustainable Medical Devices

Technological advancements are propelling medical technology towards automation through the application and widespread use of automatic control, sensing, and Internet of Things (IoT) technologies. Currently, IoT technology has been extensively applied in medical devices, aiming to ensure patient safety through more real-time detection and more effective management. In the monitoring of intravenous infusion, accurately sensing the infusion conditions in real time is particularly important. This article introduces a low-cost smart infusion device based on IoT technology, which controls the infusion rate with a peristaltic pump and monitors the volume of fluid delivered. It uses an improved, self-calibrating weighing sensor to achieve the real-time closed-loop control of the flow rate, ensuring patient safety. Additionally, the Blynk dashboard can be used for monitoring and controlling the flow rate and infusion volume.

In-situ process monitoring and adaptive quality enhancement in laser additive manufacturing: A critical review

Laser Additive Manufacturing (LAM) presents unparalleled opportunities for fabricating complex, high-performance structures and components with unique material properties. Despite these advancements, achieving consistent part quality and process repeatability remains challenging. This paper provides a comprehensive review of various state-of-the-art in-situ process monitoring techniques, including optical-based monitoring, acoustic-based sensing, laser line scanning, and operando X-ray monitoring. These techniques are evaluated for their capabilities and limitations in detecting defects within Laser Powder Bed Fusion (LPBF) and Laser Directed Energy Deposition (LDED) processes. Furthermore, the review discusses emerging multisensor monitoring and machine learning (ML)-assisted defect detection methods, benchmarking ML models tailored for in-situ defect detection. The paper also discusses in-situ adaptive defect remediation strategies that advance LAM towards zero-defect autonomous operations, focusing on real-time closed-loop feedback control and defect correction methods. Research gaps such as the need for standardization, improved reliability and sensitivity, and decision-making strategies beyond early stopping are highlighted. Future directions are proposed, with an emphasis on multimodal sensor fusion for multiscale defect prediction and fault diagnosis, ultimately enabling self-adaptation in LAM processes. This paper aims to equip researchers and industry professionals with a holistic understanding of the current capabilities, limitations, and future directions in in-situ process monitoring and adaptive quality enhancement in LAM.

Control-oriented modelling of wind direction variability

Abstract. Wind direction variability significantly affects the performance and lifetime of wind turbines and wind farms. Accurately modelling wind direction variability and understanding the effects of yaw misalignment are critical towards designing better wind turbine yaw and wind farm flow controllers. This review focuses on control-oriented modelling of wind direction variability, which is an approach that aims to capture the dynamics of wind direction variability for improving controller performance over a complete set of farm flow scenarios, performing iterative controller development and/or achieving real-time closed-loop model-based feedback control. The review covers various modelling techniques, including large eddy simulations (LESs), data-driven empirical models, and machine learning models, as well as different approaches to data collection and pre-processing. The review also discusses the different challenges in modelling wind direction variability, such as data quality and availability, model uncertainty, and the trade-off between accuracy and computational cost. The review concludes with a discussion of the critical challenges which need to be overcome in control-oriented modelling of wind direction variability, including the use of both high- and low-fidelity models.

Tracking subjects' strategies in behavioural choice experiments at trial resolution.

Investigating how, when, and what subjects learn during decision-making tasks requires tracking their choice strategies on a trial-by-trial basis. Here, we present a simple but effective probabilistic approach to tracking choice strategies at trial resolution using Bayesian evidence accumulation. We show this approach identifies both successful learning and the exploratory strategies used in decision tasks performed by humans, non-human primates, rats, and synthetic agents. Both when subjects learn and when rules change the exploratory strategies of win-stay and lose-shift, often considered complementary, are consistently used independently. Indeed, we find the use of lose-shift is strong evidence that subjects have latently learnt the salient features of a new rewarded rule. Our approach can be extended to any discrete choice strategy, and its low computational cost is ideally suited for real-time analysis and closed-loop control.

Real-time closed-loop control of molten pool transient area in direct laser deposition via PID algorithm with enhanced robustness

Real-time closed-loop control of molten pool transient area in direct laser deposition via PID algorithm with enhanced robustness

Selective recording of physiologically evoked neural activity in a mixed autonomic nerve using a minimally invasive array.

Real-time closed-loop control of neuromodulation devices requires long-term monitoring of neural activity in the peripheral nervous system. Although many signal extraction methods exist, few are both clinically viable and designed for extracting small signals from fragile peripheral visceral nerves. Here, we report that our minimally invasive recording and analysis technology extracts low to negative signal to noise ratio (SNR) neural activity from a visceral nerve with a high degree of specificity for fiber type and class. Complex activity was recorded from the rat pelvic nerve that was physiologically evoked during controlled bladder filling and voiding, in an extensively characterized in vivo model that provided an excellent test bed to validate our technology. Urethane-anesthetized male rats (n = 12) were implanted with a four-electrode planar array and the bladder instrumented for continuous-flow cystometry, which measures urodynamic function by recording bladder pressure changes during constant infusion of saline. We demonstrated that differential bipolar recordings and cross-correlation analyses extracts afferent and efferent activity, and discriminated between subpopulations of fibers based on conduction velocity. Integrated Aδ afferent fiber activity correlated with bladder pressure during voiding (r2: 0.66 ± 0.06) and was not affected by activating nociceptive afferents with intravesical capsaicin (r2: 0.59 ± 0.14, P = 0.54, and n = 3). Collectively, these results demonstrate our minimally invasive recording and analysis technology is selective in extracting mixed neural activity with low/negative SNR. Furthermore, integrated afferent activity reliably correlates with bladder pressure and is a promising first step in developing closed-loop technology for bladder control.

Towards DevOps for Cyber-Physical Systems (CPSs): Resilient Self-Adaptive Software for Sustainable Human-Centric Smart CPS Facilitated by Digital Twins

The Industrial Revolution drives the digitization of society and industry, entailing Cyber-Physical Systems (CPSs) that form ecosystems where system owners and third parties share responsibilities within and across industry domains. Such ecosystems demand smart CPSs that continuously align their architecture and governance to the concerns of various stakeholders, including developers, operators, and users. In order to satisfy short- and long-term stakeholder concerns in a continuously evolving operational context, this work proposes self-adaptive software models that promote DevOps for smart CPS. Our architectural approach extends to the embedded system layer and utilizes embedded and interconnected Digital Twins to manage change effectively. Experiments conducted on industrial embedded control units demonstrate the approach’s effectiveness in achieving sub-millisecond real-time closed-loop control of CPS assets and the simultaneous high-fidelity twinning (i.e., monitoring) of asset states. In addition, the experiments show practical support for the adaptation and evolution of CPS through the dynamic reconfiguring and updating of real-time control services and communication links without downtime. The evaluation results conclude that, in particular, the embedded Digital Twins can enhance CPS smartness by providing service-oriented access to CPS data, monitoring, adaptation, and control capabilities. Furthermore, the embedded Digital Twins can facilitate the seamless integration of these capabilities into current and future industrial service ecosystems. At the same time, these capabilities contribute to implementing emerging industrial services such as remote asset monitoring, commissioning, and maintenance.

Closed-loop control of the longitudinal axis compensation point in the K-Rb-21Ne comagnetometer

Magnetic field drift is a significant factor that influences the long-term stability of the spin-exchange relaxation-free (SERF) comagnetometer. This work proposes a real-time closed-loop control method for the longitudinal compensation point of the comagnetometer. By developing a model of the transverse (x and y axes) oscillation magnetic field response of the K-Rb-21Ne comagnetometer, the relationship between the demodulation phase at a fixed driving frequency and the longitudinal (z-axis) remanence of the comagnetometer are provided in this paper. Based on this equation, a real-time closed-loop control system for the longitudinal axis compensation point is constructed, and the effectiveness of the closed-loop control method is verified by experiments. The experimental results show that the Allan variance at 100 s is reduced by 38.2% and the repeatability of the scale factor is improved by 83.3% in the closed-loop mode relative to the open-loop mode. The research work in this paper is crucial for advancing comagnetometer engineering applications.

Improving process stability of electron beam directed energy deposition by closed-loop control of molten pool

As regards wire-based additive manufacturing technologies, including electron beam directed energy deposition (EB-DED), it is imperative to improve their process stability to promote their wide application. In the extreme non-equilibrium EB-DED process, the molten pool, a key factor for the stability and continuity of deposition, is susceptible to interference. The unique vacuum condition resulting in strong metal evaporation and adverse heat dissipation also brings challenges in introducing auxiliary means for enhancing its stability. In this study, we developed a real-time closed-loop control method to improve the stability of molten pool size by manipulating the electron beam current. We initially focused on investigating the behavior of molten pool width under different beam current variation laws and developed a relationship model between them for controller design. We then proposed a segmented control strategy considering the relationship model, the nonlinear process, the natural fluctuation of the molten pool, and the response capability of the EB-DED equipment. Our strategy and controller were tested and optimized through resisting interference and tracking input changes experiments. We further analyzed the mechanism of molten pool size variation under interferences and controlling, the origin of the natural fluctuation, and the influence of the droplet transfer. Our study improves the stability of the molten pool under potential interferences by closed-loop control, promotes the understanding of the interaction between the molten pool and electron beam, and lays the groundwork for enhancing the consistency and repeatability of the EB-DED process.

Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches

Optimizing the production of recombinant proteins is a problem of major industrial and pharmaceutical importance. Secretion of the protein by the host cell considerably simplifies downstream purification processes. However, for many proteins, this is also the limiting production step. Current solutions involve extensive engineering of the chassis cell to facilitate protein trafficking and limit protein degradation triggered by excessive secretion-associated stress. Here, we propose instead a regulation-based strategy in which induction is dynamically adjusted to an optimal strength based on the current stress level of the cells. Using a small collection of hard-to-secrete proteins, a bioreactor-based platform with automated cytometry measurements, and a systematic assay to quantify secreted protein levels, we demonstrate that the secretion sweet spot is indicated by the appearance of a subpopulation of cells that accumulate high amounts of proteins, decrease growth, and face significant stress, that is, experience a secretion burnout. In these cells, adaptations capabilities are overwhelmed by a too strong production. Using these notions, we show for a single-chain antibody variable fragment that secretion levels can be improved by 70% by dynamically keeping the cell population at optimal stress levels using real-time closed-loop control.

Real-time LIBS monitoring of laser-based layered controlled paint removal from aircraft skin based on random forest.

Laser-induced breakdown spectroscopy (LIBS) is expected to be used for real-time monitoring and closed-loop control of laser-based layered controlled paint removal (LLCPR) from aircraft skin. However, the LIBS spectrum must be rapidly and accurately analyzed, and the monitoring criteria should be established based on machine learning algorithms. Hence, this study develops a self-built LIBS monitoring platform for the paint removal process utilizing a high-frequency (kilohertz-level) nanosecond infrared pulsed laser and collects the LIBS spectrum during the laser removal process of the top coating (TC), primer (PR), and aluminum substrate (AS). After subtracting the spectrum's continuous background and screening the key features, we construct a classification model of three types of spectra (TC, PR, and AS) based on a random forest algorithm, and the real-time monitoring criterion based on the classification model and multiple LIBS spectra was established and verified experimentally. The results show that the classification accuracy is 98.89%, the time-consuming classification is about 0.03ms per spectrum, and the monitoring results of the paint removal process are consistent with the macroscopic observation and microscopic profile analysis results of the samples. Overall, this research provides core technical support for the real-time monitoring and closed-loop control of LLCPR from aircraft skin.

Real-time process monitoring and closed-loop control on laser power via a customized laser powder bed fusion platform

Additive manufacturing (AM) is one of the most effective ways to fabricate parts with complex geometries using various materials. However, AM also suffers from printing quality issues resulting from the defects such as over-melt, lack of fusion, swelling, etc. One of the root causes of those issues is that the process parameters remain constant during the entire printing process, regardless of the dynamic heat accumulation and various printing feature sizes. For instance, raster is the most common scanning strategy in the laser powder bed fusion (L-PBF) process. The length of the raster line varies depending on the printing feature size. When scanning small features, the raster line is short, resulting in heat accumulations and over-melt. These variabilities may cause severe quality issues and thus suggest adaptive process parameters be applied. Aiming to address this challenge, this study develops a closed-loop control system to regulate the laser power based on melt pool thermal emission to avoid over-melt, balling, and high surface roughness. The control target is determined by correlating the printing quality (dimensional printing error in this study) with the thermal emission through thin-line printing trials using variable power. A high-speed thermal sensor and controller are designed, tuned, and implemented on a newly developed L-PBF testbed. The system successfully maintains a low dimensional error by regulating the laser power at 2 kHz. A significant improvement in printing quality was achieved, as validated by both microscopic imaging and 3D scanning.

A Review of Thermal Spectral Imaging Methods for Monitoring High-Temperature Molten Material Streams.

Real-time closed-loop control of metallurgical processes is still in its infancy, mostly based on simple models and limited sensor data and challenged by extreme temperature and harsh process conditions. Contact-free thermal imaging-based measurement approaches thus appear to be particularly suitable for process monitoring. With the potential to generate vast amounts of accurate data in real time and combined with artificial intelligence methods to enable real-time analysis and integration of expert knowledge, thermal spectral imaging is identified as a promising method offering more robust and accurate identification of key parameters, such as surface temperature, morphology, composition, and flow rate.

Ensemble-Based Flow Field Estimation Using the Dynamic Wind Farm Model FLORIDyn

Wind farm control methods allow for a more flexible use of wind power plants over the baseline operation. They can be used to increase the power generated, to track a reference power signal or to reduce structural loads on a farm-wide level. Model-based control strategies have the advantage that prior knowledge can be included, for instance by simulating the current flow field state into the near future to take adequate control actions. This state needs to describe the real system as accurately as possible. This paper discusses what state estimation methods are suitable for wind farm flow field estimation and how they can be applied to the dynamic engineering model FLORIDyn. In particular, we derive an Ensemble Kalman Filter framework which can identify heterogeneous and changing wind speeds and wind directions across a wind farm. It does so based on the power generated by the turbines and wind direction measurements at the turbine locations. Next to the states, this framework quantifies uncertainty for the resulting state estimates. We also highlight challenges that arise when ensemble methods are applied to particle-based flow field simulations. The development of a flow field estimation framework for dynamic low-fidelity wind farm models is an essential step toward real-time dynamic model-based closed-loop wind farm control.

Neural Network-Based Confinement Mode Prediction for Real-Time Disruption Avoidance

Reliable disruption avoidance techniques are critical for the development of safe and economically viable fusion reactors. This incipient need has driven the fusion community to spend substantial effort to develop machine learning (ML) models that aim to assist in this area. One area of concern is a sudden loss in global confinement associated with an unintended back-transition from H-mode confinement to L-mode confinement (“H–L back-transition”), which can sometimes lead to a disruption and increased risk of damage to a reactor-grade fusion device. A recent experiment on DIII-D demonstrated a real-time control method able to steer away from such unwanted back transitions. The present confinement mode of the plasma was inferred in real-time using a temporal convolutional neural network that outputs a continuous scalar estimate suitable for input to a closed-loop controller. This scalar estimate was then regulated to a value consistent with H-mode by adjusting power associated with the ratio of the plasma pressure to magnetic pressure ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta _{N}$ </tex-math></inline-formula> ). The model was trained on hundreds of thousands of discrete time steps of expert-labeled data of periods of H-mode and L-mode confinement, taken from hundreds of DIII-D tokamak discharges. This work presents these experimental results, including the method of collecting expert-labeled H- and L-mode confinement data, and the ML model performance and efficacy on the mode prediction task. In addition, the first application of this tool for H–L back-transition prevention is demonstrated in this experiment. Details are presented on the training of the neural network model, including concerns such as hyperparameter tuning and the network architecture. In addition, the methodology for embedding the neural network into the control system for real-time inference and closed-loop control is discussed.

Non-invasive brain-spine interface: Continuous control of trans-spinal magnetic stimulation using EEG.

Brain-controlled neuromodulation has emerged as a promising tool to promote functional recovery in patients with motor disorders. Brain-machine interfaces exploit this neuromodulatory strategy and could be used for restoring voluntary control of lower limbs. In this work, we propose a non-invasive brain-spine interface (BSI) that processes electroencephalographic (EEG) activity to volitionally control trans-spinal magnetic stimulation (ts-MS), as an approach for lower-limb neurorehabilitation. This novel platform allows to contingently connect motor cortical activation during leg motor imagery with the activation of leg muscles via ts-MS. We tested this closed-loop system in 10 healthy participants using different stimulation conditions. This BSI efficiently removed stimulation artifacts from EEG regardless of ts-MS intensity used, allowing continuous monitoring of cortical activity and real-time closed-loop control of ts-MS. Our BSI induced afferent and efferent evoked responses, being this activation ts-MS intensity-dependent. We demonstrated the feasibility, safety and usability of this non-invasive BSI. The presented system represents a novel non-invasive means of brain-controlled neuromodulation and opens the door towards its integration as a therapeutic tool for lower-limb rehabilitation.

Closed-loop optogenetic control of the dynamics of neural activity in non-human primates.

Electrical neurostimulation is effective in the treatment of neurological disorders, but associated recording artefacts generally limit its applications to open-loop stimuli. Real-time and continuous closed-loop control of brain activity can, however, be achieved by pairing concurrent electrical recordings and optogenetics. Here we show that closed-loop optogenetic stimulation with excitatory opsins enables the precise manipulation of neural dynamics in brain slices from transgenic mice and in anaesthetized non-human primates. The approach generates oscillations in quiescent tissue, enhances or suppresses endogenous patterns in active tissue and modulates seizure-like bursts elicited by the convulsant 4-aminopyridine. A nonlinear model of the phase-dependent effects of optical stimulation reproduced the modulation of cycles of local-field potentials associated with seizure oscillations, as evidenced by the systematic changes in the variability and entropy of the phase-space trajectories of seizures, which correlated with changes in their duration and intensity. We also show that closed-loop optogenetic neurostimulation could be delivered using intracortical optrodes incorporating light-emitting diodes. Closed-loop optogenetic approaches may be translatable to therapeutic applications in humans.