Real-time Control Research Articles

IoT-Enabled Real-Time Traffic Monitoring and Control Management for Intelligent Transportation Systems

IoT-Enabled Real-Time Traffic Monitoring and Control Management for Intelligent Transportation Systems

Real-time Finite Element Analysis based on surrogate model

In the field of engineering, Finite Element Analysis (FEA) serves as a pivotal numerical simulation tool. Nevertheless, its inherent offline nature and dependency on real-time data pose limitations on its versatility. To confront this challenge, a real-time FEA approach centered on surrogate modeling has been developed. This method enables the instantaneous prediction of critical performance parameters, thereby providing crucial support for real-time decision-making and process monitoring. In the context of this study, the focus is on the glass compression molding process, a process that is not easily observable. The research commences with preliminary thermal conduction simulations conducted using ABAQUS software. Subsequently, a surrogate model is constructed to facilitate real-time FEA for the temperature field of the glass preform. Finally, Unity 3D software is harnessed for visualization, offering an effective tool for the real-time monitoring and control of the glass-forming process.

A decentralized federated learning based interoperable and heterogeneity aware predictive optimization method for energy and comfort in smart homes environment

It is a known fact that a comfortable residential space or workspace is crucial to productivity and well-being. In smart home environments, sensors can be used to sense environment and occupant data and actuators can be controlled accordingly for maximizing thermal comfort. However, keeping in view the growing demand of energy and scarcity of energy generation sources, it is mandatory to optimize the energy usage complementary to optimize thermal comfort. In order for the developed method to be interoperable and heterogeneity aware across different edge device vendors and for ease of hardware connectivity in smart homes, a REST API based framework is developed. Sensors mounted with IoT devices collect data from its respective smart home environment and store the data in its local database. An edge server is used to control the connectivity among these IoT devices and to initiate the decentralized federated learning mechanism. Each IoT device train local deep learning models for predicting energy usage, indoor temperature, indoor humidity for maintaining a comfortable environment state using minimal energy. Models from each IoT devices are aggregated at each IoT device iteratively to make federated learning independent of central server. Global deep learning model is iteratively trained for each prediction requirement. Later, these models are used to predict indoor temperature, humidity and energy consumption for time stamp (t+1), keeping in view environmental and occupant related sensed data at time stamp (t). Optimization of energy consumption and thermal comfort is mathematically modeled and based on the predictions of DL models, environment is controlled by handling actuators using a particle swarm optimization (PSO) mechanism. Relevant constraints are incorporated in PSO for limiting the positional variations in particles for efficient learning. The method continually learns and adapts to changes in the surroundings, providing real-time monitoring and control of thermal comfort to the end users. To validate our approach, data is collected and experiments are conducted in a real-time smart home’s environment. The results are compared with those obtained without using the proposed predictive optimization mechanism. The study demonstrates the effectiveness of proposed predictive optimization mechanism in providing a comfortable and productive smart home or workspace environment utilizing 38% less energy as compared to competitor.

Autonomous Needle Navigation in Subretinal Injections via iOCT.

Subretinal injection is an effective method for direct delivery of therapeutic agents to treat prevalent subretinal diseases. Among the challenges for surgeons are physiological hand tremor, difficulty resolving single-micron scale depth perception, and lack of tactile feedback. The recent introduction of intraoperative Optical Coherence Tomography (iOCT) enables precise depth information during subretinal surgery. However, even when relying on iOCT, achieving the required micron-scale precision remains a significant surgical challenge. This work presents a robot-assisted workflow for high-precision autonomous needle navigation for subretinal injection. The workflow includes online registration between robot and iOCT coordinates; tool-tip localization in iOCT coordinates using a Convolutional Neural Network (CNN); and tool-tip planning and tracking system using real-time Model Predictive Control (MPC). The proposed workflow is validated using a silicone eye phantom and ex vivo porcine eyes. The experimental results demonstrate that the mean error to reach the user-defined target and the mean procedure duration are within an acceptable precision range. The proposed workflow achieves a 100% success rate for subretinal injection, while maintaining scleral forces at the scleral insertion point below 15mN throughout the navigation procedures.

Deep-Learning-Based Strong Ground Motion Signal Prediction in Real Time

Processing ground motion signals at early stages can be advantageous for issuing public warnings, deploying first-responder teams, and other time-sensitive measures. Multiple Deep Learning (DL) models are presented herein, which can predict triaxial ground motion accelerations upon processing the first-arriving 0.5 s of recorded acceleration measurements. Principal Component Analysis (PCA) and the K-means clustering algorithm were utilized to cluster 17,602 accelerograms into 3 clusters using their metadata. The accelerograms were divided into 1 million input–output pairs for training, 100,000 for validation, and 420,000 for testing. Several non-overlapping forecast horizons were explored (1, 10, 50, 100, and 200 points). Various architectures of Artificial Neural Networks (ANNs) were trained and tested, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and CNN-LSTMs. The utilized training methodology applied different aspects of supervised and unsupervised learning. The LSTM model demonstrated superior performance in terms of short-term prediction. A prediction horizon of 10 timesteps in the future with a Root Mean Squared Error (RMSE) value of 8.43 × 10−6 g was achieved. In other words, the LSTM model exhibited a performance improvement of 95% compared to the baseline benchmark, i.e., ANN. It is worth noting that all the considered models exhibited acceptable real-time performance (0.01 s) when running in testing mode. The CNN model demonstrated the fastest computational performance among all models. It predicts ground accelerations under 0.5 ms on an Intel Core i9-10900X CPU (10 cores). The models allow for the implementation of real-time structural control responses via intelligent seismic protection systems (e.g., magneto-rheological (MR) dampers).

Ancillary services from wind and solar energy in modern power grids: A comprehensive review and simulation study

Renewable energy sources like wind and solar have increased demand for surplus power capacity. The demand is primarily fueled by the growing impact of forecasting errors associated with these intermittent energy sources. Implementing advanced control methods for automatic generation control (AGC) is essential to integrate wind and solar power with conventional generation sources to balance the power system and reduce reliance on traditional reserves. Therefore, this paper comprehensively overviews solar and wind energy integration in the AGC framework to provide optimal grid ancillary services. Initially, the paper presents an overview of the basic equations used to integrate reserve power from the photovoltaic (PV) system by employing the de-loading strategy. Subsequently, a comprehensive review is conducted on integrating the PV system in AGC strategies to provide grid ancillary services. The study also analyzes the contribution of wind power in AGC services using relevant equations and past practices. The paper presents a real-time dynamic control strategy to optimize the dispatch of the AGC unit by integrating the operating reserves from wind energy systems in conjunction with thermal power systems. The study simulates an 8-bus, 5-machine model using the Dig-SILENT Power Factory. The findings reveal that utilizing operating reserves from wind power can significantly reduce large-scale forecasting errors in massively renewable energy resources (RES) integrated power systems, thereby ensuring the necessary system operational security and reducing the reliance on traditional generating units.

Design and experiment of multidimensional and subnanometer stage driven by spatially distributed piezoelectric ceramics.

Multidimensional microdriving stage is one of the key components to realize precision driving and high-precision positioning. To meet nanometer displacement and positioning in the fields of micro-/nano-machining and precision testing, a new six-degree-of-freedom microdriving stage (6-DOF-MDS) of multilayer spatially distributed piezoelectric ceramic actuators (PZTs) is proposed and designed. The interior of the 6-DOF-MDS is a hollow design. The flexure hinge is used as the transmission mechanism, and the series-parallel hybrid driving of the corresponding PZTs achieves the microtranslation in the X, Y, and Z directions and the microrotation around the three axes of the microdriving stage, forming a microdisplacement mechanism with high rigidity and simple structure, which can realize the microfeed of 6-DOF. The force-displacement theory and lug boss structure optimization of the 6-DOF-MDS are analyzed, while the strength checking and natural frequency of the 6-DOF-MDS are also simulated by the finite element method. In addition, the real-time motion control system of the 6-DOF-MDS is designed based on Advanced RISC Machines. Through a series of verification experiments, the stroke and resolution results of the 6-DOF-MDS are obtained, where the displacements in the X, Y, and Z directions are 20.72, 20.02, and 37.60μm, respectively. The resolution is better than 0.68nm. The rotation angles around X, Y, and Z are 38.96″, 33.80″, and 27.87″, respectively, with an angular resolution of 0.063″. Relevant coupling experiments were also performed in this paper; in the full stroke linear running of X-axis, the maximum coupling displacements of the Y- and Z-axes are 1.04 and 0.17μm, respectively, with the corresponding coupling rates of ∼5.0% and 0.8%. The maximum coupling angles for the X-, Y-, and Z-axes are 0.33″, 0.14″, and 2.30″, respectively. Considering the coupling of the 6-DOF-MDS, decoupling measures and specific mathematical models have also been proposed. The proposed multidimensional microdriving stage achieves subnanometer resolution and can be used for the precise positioning and attitude control of precision instruments at the nano-/subnanometer level.

Data-driven auto-tuning strategy for RTO-MPC based on Bayesian optimization

Real-time optimization (RTO) and model predictive control (MPC) are extensively employed in industrial processes to enhance economic objectives. However, the tuning of the system remains a challenge, in particular, for cases where an explicit model relating the RTO objective and the process dynamics is unknown. To address this issue, an online data-driven auto-tuning strategy leveraging two Bayesian optimization (BO) techniques is proposed. This strategy is useful for situations where the RTO objective and the process dynamics are detectable but their exact functional forms are unclear. The proposed strategy views the RTO objective as a black-box objective and interprets the steady-state conditions as black-box equality constraints within the RTO layer. In this context, the upper-trust-bound based constrained BO (UTB-CBO) method is adopted to optimize the setpoints and enhance solution feasibility. Additionally, the proposed approach can take into account measurable disturbance inputs explicitly and account for their consequential influence on objective optimization by considering disturbance variations as contextual information. Simultaneously, another contextual BO scheme is implemented to automatically tune the MPC controller parameters for improving tracking performance upon accepting the setpoints optimized by the RTO. Simulation results based on a continuous stirred-tank reactor system are given to illustrate the effectiveness of the proposed approach.

Platoon-based Control Strategy in Mixed Traffic to Improve Ramp Merging Efficiency and Fuel Reduction

Traffic congestion in ramp merge areas, which are bottleneck areas in freeway systems, has been alleviated by traditional research methods. However, with the maturation of autonomous driving technology, control methods can shift to information-based real-time control, which aids in improving ramp merging. In this paper, we propose a ramp merging control method that integrates platoon control. This method pre-allocates vehicles in mixed traffic into platoons, utilizing the lead CAV for speed guidance to lead several HDVs. The paper also introduces a scheme to redistribute downstream gaps, leveraging information sharing and automation technologies among vehicles to control the speed of mainline vehicles. The goal is to allocate excess downstream gaps to the merging area to accommodate the ramp vehicle platoon. Our simulations validate the method, comparing uncontrolled and controlled scenarios for vehicle speeds and accelerations, highlighting the potential to lower overall travel costs while maintaining flow.

A prediction and load shed-based approach of controlling a medium voltage AC/DC testbed

Shipboard power systems have previously relied on oversized generators with minimal monitoring of the output, preventing the implementation of many real-time controls. Redundant generation is implemented instead of controls, resulting in uncontrolled load sheds when capacity is exceeded, or generators fail. The naval power system community is extensively studying the advantages of distributed zonal power system architectures that allow for the buffering of multiple generation sources and loads using energy storage and regulating power electronics. Extensive sensing and controls are necessary for successful operation of a distributed power system, and they must be evaluated adjoint to representative hardware topologies. The University of Texas at Arlington (UTA) is utilising a medium voltage (MV) AC/DC testbed to examine predictive high ramp rate (PHRR) and advanced load shed (ALS) control algorithms developed and modelled by Clarkson University (CU). A single zone of a zonal power system is being emulated using UTA’s testbed. Experimental results collected after integrating the CU control strategies into the UTA testbed will be discussed here.

High-precision 3D printing of multi-branch vascular scaffold with plasticized PLCL thermoplastic elastomer

Three-dimensional (3D) printing has emerged as a transformative technology for tissue engineering, enabling the production of structures that closely emulate the intricate architecture and mechanical properties of native biological tissues. However, the fabrication of complex microstructures with high accuracy using biocompatible, degradable thermoplastic elastomers poses significant technical obstacles. This is primarily due to the inherent soft-matter nature of such materials, which complicates real-time control of micro-squeezing, resulting in low fidelity or even failure. In this study, we employ Poly (L-lactide-co-ϵ-caprolactone) (PLCL) as a model material and introduce a novel framework for high-precision 3D printing based on the material plasticization process. This approach significantly enhances the dynamic responsiveness of the start-stop transition during printing, thereby reducing harmful errors by up to 93%. Leveraging this enhanced material, we have efficiently fabricated arrays of multi-branched vascular scaffolds that exhibit exceptional morphological fidelity and possess elastic moduli that faithfully approximate the physiological modulus spectrum of native blood vessels, ranging from 2.5 to 45 MPa. The methodology we propose for the compatibilization and modification of elastomeric materials addresses the challenge of real-time precision control, representing a significant advancement in the domain of melt polymer 3D printing. This innovation holds considerable promise for the creation of detailed multi-branch vascular scaffolds and other sophisticated organotypic structures critical to advancing tissue engineering and regenerative medicine.

Smart Agriculture Management System: Integration of IoT Technology and Mobile Application for Enhanced Farming Practices

Abstract: This paper presents a collaborative effort between Computer Science and Electronics and Communication Engineering students aimed at developing a comprehensive smart agriculture management system. Traditional farming practices face challenges related to climate variability and resource optimization, highlighting the need for innovative solutions. Leveraging IoT technology and mobile application development, this project proposes a holistic approach to address these challenges. The system integrates wireless sensors, microcontrollers, cloud computing, and a user-friendly mobile application to enable real-time monitoring, control, and optimization of agricultural processes. The paper discusses the architecture, design, implementation, and performance evaluation of the Smart Agriculture Management System (SAMS), emphasizing its potential to revolutionize farming practices and enhance productivity.

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.

Real-Time Data Processing Method for CO2 Dispersion Interferometer on EAST

Abstract In this paper, a real-time data processing system is designed for the carbon dioxide dispersion interferometer (CO2-DI) on EAST to measure electron density and provide real-time feedback control. The system utilizes the parallel and pipelining capabilities of Field-Programmable Gate Array (FPGA) to digitize and process the intensity of signals from the detector. Finally, the real-time electron density signals are exported through a Digital-to-Analog Converter (DAC) module in the form of analog signals. The system has been successfully applied in the CO2-DI system to provide low-latency electron density data to the plasma control system on EAST. Experimental results of the latest campaign with long-pulse discharges on EAST (2022-2023) demonstrate that the system can respond effectively in the case of rapid density changes, which means it is reliable and accurate for future electron density calculation.

Monitoring and Controlling of Greenhouse

Abstract: The potential for revolutionizing crop production practices has drawn significant attention to the monitoring and management of greenhouse settings. With the help of greenhouses, plants may grow in a controlled environment with the right amount of light, heat, humidity, and soil moisture. However, manual monitoring and intervention—which may be timeconsuming, laLbor-intensive, and prone to human error are frequently used in traditional greenhouse management techniques. The design, implementation, and assessment of a greenhouse monitoring and controlling system built around the ESP32 controller are proposed in this research paper. In order to gather environmental data—such as temperature, humidity, soil moisture, and light intensity—that is essential for determining plant health and maximizing growth conditions, the system incorporates sensors. Farmers may remotely get real-time insights into their greenhouse operations by transmitting the acquired data to a cloud platform or central monitoring station using the ESP32's networking features. the incorporation of ESP32 controllers into greenhouse management is a noteworthy technical development that has the potential to completely transform current agricultural methods. ESP32-based systems give farmers a strong tool for maximizing agricultural productivity while reducing resource inputs and environmental effects by providing real-time monitoring and precise control of environmental conditions

Efficient data-driven event-triggered predictive control for cyber-physical systems

As a multi-dimensional complex system that integrates computing, network, and physical environments, cyber-physical system (CPS) involves a large amount of system data in its real-time control process. The efficiency of data processing and transmission in CPS plays an important role in its control performance, however, few results have addressed such problems. This paper proposes a novel data-driven event-trigger mechanism to alleviate the data processing and transmission load for the data-driven predictive control method in CPS. The design of the event-triggered law can be transformed into solving a linear matrix inequality (LMI) based on the measured system data, while ensuring the stability of the integrated system. The improved performances are illustrated by the explicit simulation results, which indicates its potential application in CPS.

Application of Radar-Based Precipitation Data Improves the Effectiveness of Urban Inundation Forecasting

Using radar to estimate and forecast precipitation as input for hydrological models has become increasingly popular in recent years because of its superior spatial and temporal simulation compared with using rain gauge data. This study used radar-based quantitative precipitation estimation (QPE) to select the optimal parameter set for the MIKE URBAN hydrological model and radar-based quantitative precipitation forecasting (QPF) to simulate inundation in Nam Dinh city, Vietnam. The results show the following: (1) radar has the potential to improve the modeling and provide the data needed for real-time smart control if proper bias adjustment is obtained and the risk of underestimated flows after heavy rain is minimized, and (2) the MIKE URBAN model used to calculate two simulation scenarios with rain gauge data and QPE data showed effectiveness in combining the application of radar-based precipitation for the forecasting and warning of urban floods in Nam Dinh city. The results in Scenario 2 with rainfall forecast data from radar provide better simulation results. The average relative error in Scenario 2 is 9%, while the average relative error in Scenario 1 is 15%. Using the grid radar-based precipitation forecasting as input data for the MIKE URBAN model significantly reduces the error between the observed water depth and the simulated results compared with the case using an input rain gauge measured at Nam Dinh station (the difference in inundation level of Scenario 2 using radar-based precipitation is 0.005 m, and it is 0.03 m in Scenario 1). The results obtained using the QPE and QPF radar as input for the MIKE URBAN model will be the basis for establishing an operational forecasting system for the Northern Delta and Midland Regional Hydro-Meteorological Center, Viet Nam Meteorological and Hydrological Administration.

Electric power prediction of a two-body hinge-barge wave energy converter using machine learning techniques

Investigating the electric power generated by wave energy converters (WECs) has attracted increasing attention along with the developments of wave energy converting technologies in recent years. Electric power estimation currently depends on wave-to-wire model-based numerical analysis and experimental studies of scale-down prototypes. However, traditional numerical simulations are time-consuming and expensive for power orediction of a full-scale WECs operated in real sea. More importantly, they cannot accurately predict WECs’ power performance in real time, thereby cannot be used for prediction-based real-time control. To address those problems, four machine learning techniques are employed as an alternative to traditional, complex physical modelling processes to evaluate and predict the generated electric power, based on historical experimental data of a two-body hinge-barge WEC. Comparative results of a Backpropagation (BP) Neural Network algorithm, a Long Short-Term Memory (LSTM) algorithm, a Support Vector Machine (SVM) algorithm and a Radial Basis Neural Network (RBFF) algorithm indicate that machine-learning-based approaches can accurately predict the electric performance of this complex multi-degree-of-freedom WEC, where the coefficient of determination (R2) of the BP Neural Network on the test dataset is up to 0.9987.

PH and temperature dual-responsive hollow mesoporous silica nanoparticles for drug encapsulation and delivery

In this work, pH and temperature dual-responsive drug delivery vehicles (CDs/PNVCL@HMSNs) with 57.05% drug loading efficiency were prepared. Hollow mesoporous silicon nanoparticles (HMSNs) were synthesized by selective etching. The mixed shells on the surface of HMSNs were composed of carbon dots (CDs) and poly (N-vinylcaprolactam) (PNVCL), which were linked to HMSNs via Schiff base reaction. At room environment (pH = 7.4, T = 25 °C), PNVCL was stretched, so that the model drug Doxorubicin (DOX) could be transported freely through the mesopores. In the blood stream (pH = 7.4, T = 37 °C), PNVCL was collapsed, and DOX was encapsulated to form PNVCL@HMSNs-DOX. CDs were grafted to give CDs/PNVCL@HMSNs-DOX and could further inhibit the leakage of drugs. At the acidic environment of the tumor site, Schiff base bonds broke, leading to shell shedding and DOX release. The drug release could be real-time monitored by the change in fluorescence intensity, which was confirmed by the linear fitting of the fluorescence strength of CDs and the percentage of DOX released. CDs/PNVCL@HMSNs are intriguing drug delivery systems (DDSs), which can combine real-time monitoring and control of drug release.

Design and Development of a Prototype of Web Controlled Tomato Plucking Robot using IOT

In agriculture, the integration of robotics and Internet of Things (IoT) technologies has revolutionized traditional farming practices, offering efficient solutions for tasks such as harvesting. This paper presents the design and development of a web- controlled Tomato Plucking Robot (TPR) utilizing IoT principles. The TPR consists of a robotic arm mounted on a rover platform, enhancing mobility and reach. A dedicated storage unit within the rover facilitates the collection of harvested tomatoes. The entire system is remotely operated via a web interface, enabling real-time control and monitoring from any location with internet connectivity. Additionally, an ESP32 cam module is integrated to provide live navigation views, aiding in precise maneuvering and tomato detection. This research contributes to the advancement of automated agricultural practices, offering a scalable and adaptable solution for tomato harvesting while showcasing the potential of IoT-enabled robotics in optimizing farm operations