Real-time Control Research Articles

Optimal and Quasi-Optimal Automatic Tuning of Vibration Neutralizers

Vibration neutralizers are single-degree-of-freedom devices affixed to vibrating structures in order to reduce the response at a specific troublesome harmonic excitation frequency. As this frequency may vary over time, it becomes imperative to track and adjust the neutralizer to maintain the optimal performance. Recent years have witnessed the emergence of adaptive tunable vibration neutralizers, offering real-time adjustment capabilities through external actions. Thanks to real-time control algorithms, these devices enable the automatic mitigation of vibration levels in mechanical structures. A particularly successful algorithm for the automatic tuning of these devices leverages the phase angle between the base acceleration and the neutralizer’s mass. This study critically examines the justification for employing such an algorithm and scrutinizes its optimal applicability limits, particularly in the context of viscous and structurally damped systems. The findings reveal that this algorithm accurately approximates optimum tuning for systems with low damping. Moreover, from an engineering perspective, the algorithm remains acceptable even for heavily damped structures. Through a focused and comprehensive analysis, this paper provides valuable insights into the efficacy and limitations of the phase-angle-based tuning algorithm, contributing to the advancement of adaptive vibration control strategies in smart structures.

Advancing Manufacturing Efficiency through Real-time Production Monitoring and Control Systems

The advantages of both real-time production monitoring and control systems in enhancing the advancement of manufacturing efficiency cannot be overlooked. Manufacturing industries need to meet customers’ demand while still maximizing their profits via avoiding emergency shutdown of plants during production. Nonetheless, quality products with high purity level should be supplied uninterruptedly without breakage of supply chain. In recent times, failure of many of manufacturing industries to deliver their products to end users have been cited and reported. This was due to inadequate scheduling which was not being monitored via real-time production and control systems. There is need to tackle this problem which calls for the adoption of real-time production monitoring and control systems. In this paper, the concepts behind the use of real-time production monitoring and control systems were discussed. Consideration was given to how real-time production monitoring can be integrated in manufacturing. The features of real-time control systems were discussed alongside with their industrial applications in various disciplines of Engineering. Data analysis and prognostics, data collection, visualization module and data storage were identified as the relevant sequential steps in real time production monitoring in advancing manufacturing efficiency. In conclusion, the significance and importance of real-time production monitoring and control systems in advancing manufacturing efficiency have been revealed and discussed in this paper.

Economical Design Perspective for Aircraft by Optimizing Airfoil S1223

Geographical positioning system based end computer control technology are utilized in the air modelling based dynamics models. Unmanned aircraft have the potential to significantly increase their coverage and flying precision. The observation area is effectively controlled if traditional continues pest control algorithms are used. Calculating the path phones take a long time. It is not ideal for aircraft control in real time, when Bezier curves are used to calculate the planning path point allow to minimize the calculation time in easiest way, but producing an expected target becomes difficult. When linear and circular interpolation devices which are frequently utilized in numerical, control system aid applied to the aircraft turning path control algorithm, the calculation time is reduced and the flight path becomes accurate as per objectives of this research discussed in section two. This paper combines flight course control and effects of flying speed an angle of attack on aircraft height to explain how the turning path control algorithm is implemented. The outcome results demonstrate that path control is accurate and the algorithm straight forward. Finally air photography and flight tests confirm the rationality of the design.

Embedded Systems Basics

Embedded systems play a pivotal role in modern technological advancements, integrating computing capabilities into various devices and systems. This paper presents an abstract of an embedded system designed for real-time monitoring and control applications in industrial automation. The system utilizes a combination of microcontroller-based hardware and software components to achieve reliable and efficient operation.

Real-time implementation and explainable AI analysis of delayless CNN-based selective fixed-filter active noise control

The selective fixed-filter active noise control (SFANC) approach can select suitable pre-trained control filters for different types of noise. With the learning ability of convolutional neural network (CNN), the CNN-based SFANC method can automatically learn its parameters from noise data. Combining practical experience, this paper abstracts ANC as a Markov progress and provides a detailed theoretical analysis to verify the reasonableness of the CNN-based SFANC method. To validate its effectiveness, we implement the method in a multichannel ANC window, where the CNN operating in the co-processor collaborates with the real-time controller to realize delayless noise control. Additionally, an explainable AI technique is used to analyze the underlying principle of the CNN-based SFANC method, enhancing its interpretability in acoustic applications. Numerical simulations and real-time experiments demonstrate that the CNN-based SFANC method achieves not only satisfactory noise reduction performance for broadband and real-world noises but also excellent transferability.11The code will be accessible at https://github.com/Luo-Zhengding/SFANC-Window.

Comparison of Linear and Nonlinear Model Predictive Control in Path Following of Underactuated Unmanned Surface Vehicles

Model predictive control (MPC), an extensively developed rolling optimization control method, is widely utilized in the industrial field. While some researchers have incorporated predictive control into underactuated unmanned surface vehicles (USVs), most of these approaches rely primarily on theoretical simulation research, emphasizing simulation outcomes. A noticeable gap exists regarding whether predictive control adequately aligns with the practical application conditions of underactuated USVs, particularly in addressing real-time challenges. This paper aims to fill this void by focusing on the application of MPC in the path following of USVs. Using the hydrodynamic model of USVs, we examine the details of both linear MPC (LMPC) and nonlinear MPC (NMPC). Several different paths are designed to compare and analyze the simulation results and time consumption. To address the real-time challenges of MPC, the calculation time under different solvers, CPUs, and programming languages is detailed through simulation. The results demonstrate that NMPC exhibits superior control accuracy and real-time control potential. Finally, we introduce an enhanced A* algorithm and use it to plan a global path. NMPC is then employed to follow that path, showing its effectiveness in tracking a common path. In contrast to some literature studies using the LMPC method to control underactuated USVs, this paper presents a different viewpoint based on a large number of simulation results, suggesting that LMPC is not fit for controlling underactuated USVs.

Vision-based real-time process monitoring and problem feedback for productivity-oriented analysis in off-site construction

The widespread adoption of surveillance cameras in work environments has enabled the direct and non-intrusive detection of productivity-related issues in the field of construction. In this research, a process monitoring and problem feedback framework is developed based on closed-circuit television footage and computer vision analysis to achieve real-time visual control of the work process and facilitate data-driven decision-making in off-site construction. To enhance the automation of productivity-related problem recognition, a novel video analysis algorithm is developed to process the inputted video footage and provide feedback with respect to nine productivity issues. The z-score and Exponential Moving Average methods are employed to eliminate detection errors, and the spatial density analysis method is adopted to visually analyze spatial information. The observed performance of the proposed framework demonstrates that it can accurately acquire data from footage and provide process monitoring and problem feedback in real time.

Dynamic wind farm flow control using free-vortex wake models

Abstract. A novel dynamic economic model-predictive control strategy is presented that improves wind farm power production and reduces the additional demands of wake steering on yaw actuation when compared to an industry state-of-the-art reference controller. The novel controller takes a distributed approach to yaw control optimisation using a free-vortex wake model. An actuator-disc representation of the wind turbine is employed and adapted to the wind farm scale by modelling secondary effects of wake steering and connecting individual turbines through a directed graph network. The economic model-predictive control problem is solved on a receding horizon using gradient-based optimisation, demonstrating sufficient performance for realising real-time control. The novel controller is tested in a large-eddy simulation environment and compared against a state-of-the-art look-up table approach based on steady-state model optimisation and an extension with wind direction preview. Under realistic variations in wind direction and wind speed, the preview-enabled look-up table controller yielded the largest gains in power production. The novel controller based on the free-vortex wake produced smaller gains in these conditions while yielding more power under large changes in wind direction. Additionally, the novel controller demonstrated potential for a substantial reduction in yaw actuator usage.

Design, FEM analysis and development of switched reluctance motor electric drive for TRI-wheeler E-vehicle application

Electric vehicles have seen substantial growth in recent years in the emerging economies of the world. The current market scenario of E-vehicles is dominated by PMSM or BLDC motors due to their higher torque-to-power density, and efficiency with added performance metrics of the motor. However, the higher capital cost of permanent magnets makes the motor designers to consider an alternative to magnetic motors, the so-called Switched Reluctance Motors (SRM). With proper FEM analysis and controller design, if SRMs can able to achieve the performance metrics of a PMSM/BLDC, it will be a breakthrough in the automotive sector with affordable prices. Based on the aforementioned facts, this research work focuses on SRM as a viable alternative to the existing PMSM/BLDC motor. The work concentrates on the design and development of an 8/6 SRM for a Tri-wheeler E-vehicle application. The designed motor is fabricated, tested and controlled by WAVECT, an FPGA-based real-time digital controller which is used to control the commutation of the 8/6 SRM. The yielded response satisfactorily proves that the designed 8/6 SRM can be effectively used for the Tri-wheeler E-vehicle application, thereby paving the way for sustainable energy conservation in the automotive sector.

Carbon Footprint Reduction for Sustainable Data Centers in Real-Time

As machine learning workloads are significantly increasing energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide. This requires a paradigm shift in optimizing power consumption in cooling and IT loads, shifting flexible loads based on the availability of renewable energy in the power grid, and leveraging battery storage from the uninterrupted power supply in data centers, using collaborative agents. The complex association between these optimization strategies and their dependencies on variable external factors like weather and the power grid carbon intensity makes this a hard problem. Currently, a real-time controller to optimize all these goals simultaneously in a dynamic real-world setting is lacking. We propose a Data Center Carbon Footprint Reduction (DC-CFR) multi-agent Reinforcement Learning (MARL) framework that optimizes data centers for the multiple objectives of carbon footprint reduction, energy consumption, and energy cost. The results show that the DC-CFR MARL agents effectively resolved the complex interdependencies in optimizing cooling, load shifting, and energy storage in real-time for various locations under real-world dynamic weather and grid carbon intensity conditions. DC-CFR significantly outperformed the industry-standard ASHRAE controller with a considerable reduction in carbon emissions (14.5%), energy usage (14.4%), and energy cost (13.7%) when evaluated over one year across multiple geographical regions.

ICG-Enhanced Compression Anatomical Segment 7 Segmentectomy in Laparoscopic and Robotic Approach.

Minimally invasive anatomical resection (AR) for posterosuperior lesions is technically challenging.1,2 The Glissonean approach or puncture technique is generally selected.3,4 The tumor-feeding portal pedicle compression AR (C-AR) is an established procedure in open surgery.5 This technique has benefited from the association with indocyanine green (ICG) fluorescence, used to enhance the anatomical area to be resected.6 Recently, C-AR via the minimal access approach has been reported.7 Herein, we report the first cases of laparoscopic and robotic segment 7 (S7) segmentectomy using the ICG-enhanced compression technique. Two cases of CHILD-class A hepatocellular carcinoma (HCC) in segment 7 with a liver stiffness less than 7 kPa treated by laparoscopic and robotic anatomical S7 segmentectomies were reported. Using the intraoperative ultrasound (IOUS), the tumor-bearing portal pedicle and the level targeted for compression were identified. The right hemiliver was adequately mobilized to allow handling of the organ during dissection. Using the grasper and the probe itself, the S7 Glissonean pedicle was transparenchymally compressed under real-time IOUS control. To further enhance the visibility of the discolored S7, ICG was administered intravenously, obtaining the compressed area to be resected as a non-stained one. Dissection was performed under intermittent Pringle maneuver up to exposing the right hepatic vein, dividing the Glissonean pedicle to segment 7 and then completing the resection. Pathologic findings demonstrated a 4.9 cm and 7.3 cm HCC with a R0-resection margin (> 1 cm in both). Postoperative complications were nil. The patients were discharged 6 days after surgery. This preliminary experience shows that the C-AR is a feasible and reliable technique in laparoscopic and robotic approach for posterosuperior lesions. Further studies are needed to investigate its applicability and standardization.

Networked explicit hybrid predictive control of an industrial benchmark process

Hybrid systems have acquired deep attention in research as many systems in industries and laboratories manifest hybrid dynamic behaviors. Optimal control design for hybrid systems exhibits challenges for controlling the interacting continuous and discrete dynamics. A double-tank hybrid system (DTHS) is proposed in this paper with a mixed-integer actuation mechanism. The proposed DTHS aims to control the liquid level of the two tanks simultaneously by a networked hybrid predictive controller. Hybrid frameworks are provided to investigate the system through reachability analysis. An explicit hybrid model predictive control (EHMPC) has been developed to control the levels of the DTHS. Hardware-In-the-Loop (HIL) simulation is performed for assessing the performance of the EHMPC with a hardware real-time networked controller. The networked real-time controller is constructed and implemented using the Node.js frameworks. The simulation and realtime results are presented and discussed to evaluate the hybrid controller performance.

Petri Net Model Predictive Control Method for Batch Chemical Systems

In order to address the problem of the real-time scheduling and control of batch chemical systems, this work proposes a model predictive control method based on Petri nets. First, a method is presented to construct a batch chemical system’s timed Petri net model. Second, a control structure is designed to augment the Petri net model to control the valves. This results in timed Petri nets that formally represent the process specifications of a batch chemical system. Third, a model predictive control method is developed to schedule and control timed Petri nets, where a proposed heuristic function is utilized to perform the optimization computation. The model parameters are dynamically adjusted using online data, and both scheduling and valve control instructions are calculated in real time. Finally, a series of experiments is carried out in a beer canning plant to verify the proposed method. According to the experimental results, the scheduling and control problem can be solved in real time, where the online computations can be performed in milliseconds, and the resulting scheduling strategies are optimal or near-optimal.

A global optimization method for data center air conditioning water systems based on predictive optimization control

As the national digitization process accelerates, the number and energy consumption of data centers are increasing year by year. Want to reduce the PUE of the data center. It is correct to start from the perspective of optimizing the energy consumption of air conditioning system operation. In this paper, a predictive optimization control method that is different from previous real-time optimization control is proposed. Predict, optimize, control and adjust the operating parameters of the air conditioning water system in the data center. At the same time, it solves the energy saving problem of the air conditioning system and the lag problem of previous adjustments. Predictive optimization control has broad prospects in research on operation optimization of data center air conditioning water systems.

Smart Ride Connect (Revolutionizing Bus Pass Management through Innovative Transportation Solutions)

Smart Ride Connect introduces aparadigm shift in bus pass management by harnessing the power ofQrCodetechnology.Thesystemaimstostreamlinethecumbersomeprocessofissuing, validating, and managing bus passes, thereby enhancing the over all efficiency and user experience of public transportation systems. By providing personalized QR Code-enabled passes with essential user information and validity details encoded within, Smart Ride Connect ensures seamless access to transportation services while empowering administrators with real-time insights and control over the system Keywords: Bus pass managements, QR Code technology, Streamline, Issuing, Public transportation, QR Code-enabled passes, Validity details

Swirl-induced motion prediction with physics-guided machine learning utilizing spatiotemporal flow field structure

Purpose Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions. Design/methodology/approach To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework. Findings The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process. Originality/value The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

SP2D: Simplified pseudo 2D battery model by piecewise sinusoidal/quadratic functions of potential curves

The pseudo 2D battery model is a widely used tool for analysing the electrochemical behaviour of lithium-ion batteries. However, the high computational expense of solving the fully coupled governing equations is a significant challenge. In this study, a simplified pseudo 2D battery model has been developed to address this bottleneck. The model gets the electrolyte potential with relative potential plus reference potential, which are described by piecewise quadratic or sinusoidal function and determined by boundary conditions. Two electrodes are defined with uniform potentials. This approach provides a simple solution of charge transfer equations and decouples them from the governing equations of the pseudo 2D model. It shows excellent agreement with the original pseudo 2D battery model for a wide range of C-rates, drive cycles and is applicable to electrolytes with different conductivities. The proposed approach provides a simpler and faster solution than the pseudo 2D battery model, with negligible accuracy sacrifice. Consequently, this work is crucial for applying physics-based models to real-time battery control and management systems.

A new statistical-quality-control methodology for panel line monitoring in shipyards

The construction of cruise ship hulls is a complex process that involves various stages: it begins with the fabrication of panels, which involves marking, cutting, and shaping metal plates of considerable length, followed by the welding of rigid structural components onto these panels. To minimize delays and cost overruns, it is crucial to detect any anomalies early on and rectify them through appropriate repair work. Therefore, real-time conformity verifications and effective Statistical Quality Control (SQC) methodologies are necessary. This paper proposes a novel SQC methodology, specifically tailored for monitoring the panel line in cruise-ship shipyards. This methodology, while adopting a traditional standardized p control chart with samples of variable size, integrates two original aspects: (i) it accounts for the significant level of customization and specific quality characteristics inherent in the different panels and (ii) it rigorously considers the measurement uncertainty associated with the large-volume metrology instruments (such as state-of-the-art total stations, laser trackers or laser scanners) used for conformity verification, following the ISO 14253-1:2017 standard. The methodology is exemplified through a real-world case study, providing practical insights into its application.

A metaheuristic algorithm for model predictive control of the oil-cooled motor in hybrid electric vehicles

The current energy management methods for hybrid vehicles are primarily focused on matching the power flow between the engine and the motor. In order to further reduce overall energy consumption and extend the vehicle's lifespan, this paper utilizes model predictive control (MPC) in the energy management of hybrid vehicles to control the energy consumption and temperature of the oil-cooled motor, with a focus on studying the algorithm for solving the optimization problem in MPC. The main work includes: Establishing a simplified model for the oil-cooled motor and training the working condition prediction model based on the worldwide harmonized light vehicles test cycle (WLTC) conditions utilizing long short-term memory (LSTM) recurrent neural network technology. Furthermore, proposing a novel metaheuristic algorithm based on the temporal nature of the problem, multiple hierarchical clustering algorithm (MHCA). In comparison with typical metaheuristic algorithms, the MPC optimized using MHCA exhibits significantly reduced computation time, with an average time of 0.1967s, indicating that using MHCA can theoretically achieve real-time control and eliminate lag in actual control processes. Furthermore, compared to other optimization algorithms, the MHCA algorithm demonstrates the lowest correlation between computation time and operational conditions, highlighting its high robustness in ensuring the safety of practical control.

Probabilistic state estimation in district heating grids using deep neural network

Probabilistic state estimation is critical for operating and controlling district heating grids efficiently. However, computational bottlenecks of traditional solvers limit the feasibility of uncertainty-aware Bayesian estimation. This paper proposes using deep neural networks (DNNs) to enable fast and accurate posterior estimation. Fully-connected neural networks (FCNNs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) are evaluated as candidate approximators of the physical model. Markov chain Monte Carlo sampling in the heat exchange space is leveraged to generate posterior samples. Experiments on a benchmark heating grid demonstrate FCNNs can efficiently learn the mapping from heat exchanges to network states. A FCNN trained on 20 training epochs after hyperparameter optimization provides the best approximation accuracy and uncertainty estimates, outperforming prior methods based on Deep Neural Networks. The results highlight the potential of data-driven deep learning models for probabilistic state estimation. The proposed framework could enable real-time uncertainty-aware control and decision-making for future intelligent district heating grids.