Advanced Control for Applications最新文献

{"title":"SCADA based common bus regenerative control of PMSM drive for industrial application","authors":"M. Baranidharan,&nbsp;R. Raja Singh","doi":"10.1002/adc2.199","DOIUrl":"10.1002/adc2.199","url":null,"abstract":"<p>The emerging trend in sustainable industrial processes focuses on energy-efficient drives to enhance performance in both intermediate and continuous operating conditions. For decades, researchers have focused on permanent magnet synchronous machines (PMSM) for industrial applications in order to retain consistency in performance and efficiency under wide speed ranges. Common bus regenerative control is an energy-efficient method used in industrial systems to manage power flow between multiple devices on a shared DC bus. It captures and reuses excess energy generated during braking/deceleration, reducing waste and improving overall system efficiency. The experimental behavior of the drive scheme has been observed as per the standards of the laboratory. In this article, regenerative control of a PMSM drive is using the voltage vector control (VVC+) technique and the SCADA based condition monitoring for the PMSM drive system is integrated to supervise the parameters including current, voltage, power, speed, torque, and temperature under dynamic operating conditions. As a primary step of implementing the drive condition monitoring is carried out through Danfoss's motion control tool software. Further, the SCADA control system is interfaced to FC302 PMSM drive through RS485 Modbus communication to extract the necessary electrical attributes and monitor the same.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140247713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short term power load forecasting system based on improved neural network deep learning model","authors":"Lulu Yuan","doi":"10.1002/adc2.197","DOIUrl":"10.1002/adc2.197","url":null,"abstract":"<p>The electricity load prediction is closely related to production and daily life. The electricity load prediction is also a very important task. With the widespread application of smart grids, load data shows an exponential growth trend. The huge amount of data in the load makes power prediction even more difficult. On the basis of traditional prediction algorithms, a power load prediction model based on machine learning and neural networks is designed. Because the single model prediction has the unstable results, a combined model is obtained based on the ensemble learning idea and two single model prediction method. The prediction results are detected by the load data. From the experimental results, the mean absolute percentage error (MAPE) of the AdaBoost-GRU data fusion model is 0.066%. Compared to the AdaBoost-GRU data fusion model, the MAPE decreases by 1.59% and 1.12%, respectively. The relative mass scores of the two groups decrease by 132.57% and 89.14%, respectively. The prediction accuracy is improved, which has advantages compared to traditional combination models. It can effectively enhance the accuracy of short-term power grid load forecasting. It is an important scientific and practical reference for power grid decision-making.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.197","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140253055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mission time minimization for UAV-supported data distribution in Internet of Things","authors":"Rui Liu,&nbsp;Zhenyu Na,&nbsp;Bowen Li,&nbsp;Ye Lin","doi":"10.1002/adc2.202","DOIUrl":"10.1002/adc2.202","url":null,"abstract":"<p>Unmanned aerial vehicles (UAVs) have been widely used to transmit data to Internet of Things (IoT) devices in various industrial, civil and military applications because of their flexibility and mobility. Some emergency situations pose strict requirements for UAV mission completion time. Therefore, this paper investigates a UAV-supported data distribution network, where a UAV is dispatched to distribute data to a group of IoT devices. We propose a device attribution (DA) based cluster-by-cluster (CBC) communication strategy. The objective is to minimize UAV mission time while satisfying the required data amount of all devices. To this end, we propose a mission time optimization algorithm (MTOA), whose key lies in invoking DA mechanism to determine the device belonging in the coverage of overlapping clusters. Numerical results demonstrate that the proposed strategy can effectively reduce the mission time compared with the baseline ones, offering an innovative method for solving complex device attribution issues. Furthermore, the proposed strategy is expected to exhibit a significant potential in scenarios involving the high-density IoT device deployment.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.202","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140256752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design of a robotic system to assist in the treatment of severe COVID-19 patients","authors":"Hoang T. Tran,&nbsp;Dong L. T. Tran,&nbsp;Minh T. Nguyen","doi":"10.1002/adc2.193","DOIUrl":"https://doi.org/10.1002/adc2.193","url":null,"abstract":"<p>The article presents the conceptualization, development, and implementation of a sophisticated mobile robotic system with the purpose of providing aid in the medical care of those afflicted with severe cases of COVID-19. The robotic system will engage in verbal communication with the patient and provide updates regarding the external environment. Additionally, it will facilitate the delivery of food, beverages, and other consumable items to the isolation room. The robot has the capability to navigate to its intended location using two distinct modes: autonomous mode and online control mode. The hardware is constructed within a mobile robot system that is interconnected to the Internet over a 4G mobile network. The system employs a client–server software architecture wherein the transmission of data between the client and the server occurs through various transport protocols. Experimental simulations were conducted in an active treatment room to evaluate the performance of autonomous operating mechanisms, including obstacle avoidance positioning, safe destination navigation, and feedback display for remote robot operation.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140145703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time route planning for low observable unmanned combat aerial vehicle","authors":"Yuanchao Yang","doi":"10.1002/adc2.194","DOIUrl":"https://doi.org/10.1002/adc2.194","url":null,"abstract":"<p>The next generation of low observable (LO) unmanned combat aerial vehicle (UCAV) with highly autonomy to implement a penetration mission requires advanced methods for flyable and safe route planning (i.e., respecting physical capability of vehicle and threat coverage by hostile air defense radars) at a real-time manner. Currently, the main challenge of real-time route planning for LO UCAV is to achieve computationally efficiency under dynamic (pop-up/moving) threats by air defense radars. In this paper, a real-time planning paradigm in compliance with complex penetration requirements is proposed, and a complete modeling of route planning for LO UCAV's penetration as an optimal control problem is designed. The paper at first devises a direct method to transform the optimal control problem into a nonlinear programming (NLP) problem and then solves the formulated NLP problem under a moving planning horizon. The proposed method can give computationally efficient route planning results for LO UCAV's penetration under multiple kinds of radar threats. Numerical test results based on F-16 uninhabited platform demonstrate the effectiveness of the proposed method.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.194","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140145704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI monitoring and warning system for low visibility of freeways using variable weight combination model","authors":"Minghao Mu,&nbsp;Chuan Wang,&nbsp;Xinqiang Liu,&nbsp;Haisong Bi,&nbsp;Hanlou Diao","doi":"10.1002/adc2.195","DOIUrl":"10.1002/adc2.195","url":null,"abstract":"<p>In intelligent vehicles, road environment perception technology is a key component of autonomous driving assistance systems. This component is the foundation for vehicle decision-making and control, and is a guarantee of safety during the driving of the vehicle. The existing environment perception technology mainly targets well-lit environments and requires visible light imaging equipment. Therefore, in low visibility environments, this technology cannot make good judgments about the external environment. Many existing perception systems mainly rely on sensors. Under low visibility conditions, these sensors weaken their effectiveness due to signal transmission, reflection, or absorption, resulting in incomplete or distorted data collection. Reduced visibility often affects the sensing range of various sensors, hindering the system's ability to detect and recognize distant objects, thereby limiting the necessary advance warning and response time for safe navigation. In response to this issue, this study proposed a combined method of infrared imaging and polarized imaging to collect feature data on road conditions in low visibility environments. Then, the obtained images were denoised and enhanced. The processed images were input into the system for recognition, and the images were analyzed and recognized using a low visibility road situation semantic segmentation algorithm based on deep learning. The research outcomes denoted that the pixel accuracy, average pixel accuracy, and average intersection ratio of the variable weight combination model in polarized degree images were 91.2%, 89.1%, and 71.6%, respectively. Those in infrared images were 83.6%, 90.6%, and 62.1%, respectively. The various indicators of the variable weight combination model were higher than those of the U-shaped neural network model, indicating its performance is relatively excellent. The research results indicated that infrared imaging helps to acquire information at night or in low light conditions, while polarized imaging can provide better adaptation to cluttered light and reflections, enabling the system to provide more robust environmental sensing in complex weather conditions. It fills a critical gap in perception for autonomous driving systems in adverse weather conditions.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.195","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140428307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study of oscillation characteristics for quartz crystal oscillators based on equivalent multi-physics model","authors":"Zhiyu Chen,&nbsp;Yueyan Zhu","doi":"10.1002/adc2.192","DOIUrl":"10.1002/adc2.192","url":null,"abstract":"<p>In recent years, high-performance quartz-crystal oscillators (XOs) for integrated circuits have been receiving considerable attention due to their featuring low voltage and high-frequency stability. However, recent studies tend to focus solely on the impact of temperature as a single factor on crystal oscillator circuits, overlooking the circuit structure of the crystal oscillator itself. In this paper, a novel four-parameter crystal model of XOs is detailed demonstrated, and analyzed to interpret typical XO oscillation characteristics at room temperature. The relationship between the RLC circuit and the oscillation was investigated. Meanwhile, the study delves into the various factors that influence oscillation behavior, paving the way for a comprehensive understanding of XOs' performance characteristics. temperature sweep simulations were induced to verify the theory and found that the parameter drift and thermal perturbation are close to the theory we proposed, which can be applied in temperature-compatible XOs. The significance of this study lies not only in its contribution to the design and implementation of compact footprint XOs in the oscillator circuit platform but also in its provision of experimental evidence for fabricating wide temperature range compensated XO devices. The results show that the capacitance in the equivalent model of a crystal oscillator plays a dominant role in shaping the output waveform and exhibits relatively good temperature stability characteristics and serve as a valuable resource for engineers and researchers working on improving the performance and reliability of XOs, ultimately enabling the development of more advanced and efficient integrated circuits.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.192","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140432141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification and evaluation of the evolution stage of the agricultural machinery industry cluster in Shandong Province","authors":"Qiong He,&nbsp;Qixiao Li,&nbsp;Zhenlong Wan","doi":"10.1002/adc2.191","DOIUrl":"10.1002/adc2.191","url":null,"abstract":"<p>Agricultural machinery industry clusters have great potential to solve key technological problems in China, and it is crucial to accurately identify the stage of cluster evolution. Based on the location entropy method, this paper finds that the location quotient coefficient is greater than 1.2 and the average annual growth rate is 1.11%, which indicates that the agricultural machinery industry in Shandong Province has a high degree of agglomeration, but the agglomeration speed is slow. Using the Groundings agglomeration—Economic network—Social network—Service system model, it is found that the agricultural machinery industry cluster in Shandong province is in the growth stage, in which the service system has the most significant influence on its development level. The weights of service system, social network, economic network, and basic resource aggregation derived from the Analytic Hierarchy Process model are 0.410, 0.321, 0.151, and 0.118, respectively, where agglomeration degree of the agricultural machinery industry, raw material production of agricultural machinery enterprises, exchange of tacit knowledge and intermediary service level are the four indicators with the greatest weights in the influences on sustainable development of the agricultural machinery industry. Because of the strong fuzzy nature between the indicators, this paper applies the Fuzzy Comprehensive Evaluation method to quantify the stage of evolution of Shandong Province's agricultural machinery industry cluster.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140434126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Industrial design of 3D printing technology combined with assisted medical service robots","authors":"Jing Zhang","doi":"10.1002/adc2.185","DOIUrl":"https://doi.org/10.1002/adc2.185","url":null,"abstract":"<p>With the continuous development and maturation of the era of intelligent manufacturing, there is a perpetual emergence of new information technology, control technology, and material technology, which are constantly accelerating 3D printing technology to advance to an unprecedented level. To achieve the safety perception interaction ability of auxiliary medical service robots, this study develops a direct write hybrid 3D printing auxiliary medical service robot system, enabling it to achieve temperature sensing function. Moreover, combined with linear interpolation algorithms, 3D printing technology has been improved to achieve improvement of system control accuracy. The results indicate that the apparent viscosity of the printing material Ag-TPU is still greater than 2000 Pa s at a rate of 87 s⁻¹. The change in resistance during 20% stretching is within 1.2 Ω, and the change is around 3 Ω during 30% stretching. When the preset temperature is 39.2°C, the absolute deviation is the smallest, about 0.03. When the preset temperature is 41.7°C, the maximum value is approximately 0.17. The absolute error of real-time temperature collection for auxiliary medical service robots is less than 0.2°C at temperatures ranging from 38 to 42°C. Over the past 30 days of overall operation, the system has had 970 users, 3270 interactions, and 99.4% availability. This system improves the perception and interaction ability of auxiliary medical service robots, which has certain practical potential in the field of medical services.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142860267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heating ventilation air-conditioner system for multi-regional commercial buildings based on deep reinforcement learning","authors":"Juan Yang,&nbsp;Jing Yu,&nbsp;Shijing Wang","doi":"10.1002/adc2.190","DOIUrl":"10.1002/adc2.190","url":null,"abstract":"<p>In an era of significant energy consumption by commercial building HVAC systems, this study introduces a Deep Reinforcement Learning (DRL) approach to optimize these systems in multi-zone commercial buildings, targeting reduced energy usage and enhanced user comfort. The research begins with the development of an energy consumption model for multi-zone HVAC systems, considering the complexity and uncertainty of system parameters. This model informs the creation of a novel DRL-based optimization algorithm, which incorporates multi-stage training and a multi-agent attention mechanism, enhancing stability and scalability. Comparative analysis against traditional control methods shows the proposed algorithm's effectiveness in reducing energy consumption while maintaining indoor comfort. The study presents an innovative DRL strategy for energy management in commercial HVAC systems, offering substantial potential for sustainable practices in building management.</p>","PeriodicalId":100030,"journal":{"name":"Advanced Control for Applications","volume":"6 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/adc2.190","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139808458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}