Pub Date : 2023-07-20DOI: 10.1109/ICJECE.2023.3271304
Anshuman Sharma;Mohamed Z. Youssef
In order to establish an efficient inductive power transfer (IPT) mechanism for electric vehicles (EVs) it is necessary that a system with effective power control and efficiency maximization is established. As the equivalent resistance of the on-board battery charger continuously fluctuates during operation, a battery charging algorithm based on an improvised continuous current (CC)–constant voltage (CV) is proposed. This article introduces the design of an integrated stationary IPT system to inductively transfer power from a transmitter pad positioned on the ground and the receiver pad embedded under the chassis of an EV. An innovative feature of the design is the implementation of a magnetic switch sensor that is incorporated into both the transmitting and receiving wireless charging circuitry to ensure optimum alignment for IPT. The power electronics design focuses on the implementation of an H-bridge converter incorporating series–series (SS) compensation topology to use an innovative control algorithm to prioritize battery charging operations. The system is validated through a simulation model in PSIM and a hardware-in-the-loop (HIL) simulation in Typhoon before hardware implementation and testing of the developed prototype. At a test resonant frequency of 23.74 kHz and a nominal air gap separation of 120 mm, the developed IPT system had an overall efficiency of 93.41%.
{"title":"A Magnetic Switch Sensor Based Inductive Power Transfer System With Power Control and Efficiency Maximization for Vehicular Applications","authors":"Anshuman Sharma;Mohamed Z. Youssef","doi":"10.1109/ICJECE.2023.3271304","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3271304","url":null,"abstract":"In order to establish an efficient inductive power transfer (IPT) mechanism for electric vehicles (EVs) it is necessary that a system with effective power control and efficiency maximization is established. As the equivalent resistance of the on-board battery charger continuously fluctuates during operation, a battery charging algorithm based on an improvised continuous current (CC)–constant voltage (CV) is proposed. This article introduces the design of an integrated stationary IPT system to inductively transfer power from a transmitter pad positioned on the ground and the receiver pad embedded under the chassis of an EV. An innovative feature of the design is the implementation of a magnetic switch sensor that is incorporated into both the transmitting and receiving wireless charging circuitry to ensure optimum alignment for IPT. The power electronics design focuses on the implementation of an H-bridge converter incorporating series–series (SS) compensation topology to use an innovative control algorithm to prioritize battery charging operations. The system is validated through a simulation model in PSIM and a hardware-in-the-loop (HIL) simulation in Typhoon before hardware implementation and testing of the developed prototype. At a test resonant frequency of 23.74 kHz and a nominal air gap separation of 120 mm, the developed IPT system had an overall efficiency of 93.41%.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 3","pages":"207-217"},"PeriodicalIF":0.0,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68026230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-21DOI: 10.1109/ICJECE.2023.3280333
{"title":"IEEE Canadian Journal of Electrical and Computer Engineering","authors":"","doi":"10.1109/ICJECE.2023.3280333","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3280333","url":null,"abstract":"","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 2","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9349829/10137376/10158953.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68016410","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}
Pub Date : 2023-06-19DOI: 10.1109/ICJECE.2023.3264852
Lingli Gong;Anshuman Sharma;Mohammad Abdul Bhuiya;Hilmy Awad;Mohamed Z. Youssef
This article demonstrates an innovative design of a sensorless technique to diagnose, monitor, and broadcast faults in an electric vehicle’s (EV) propulsion operating conditions. By utilizing the artificial intelligence with a signal processing mixed clustering technique, an onboard health monitoring system (HMS) has been presented. The clustering technique uses a data-mining approach to prevent future failures for predictive maintenance planning, which is novel. For example, the propulsion inverter is equipped with a diagnostic system that uses the proposed algorithm to compare the reference gate-driving signal with the actual output voltage of the voltage source inverter (VSI). This article presents different failure scenarios of the inverter and demonstrates the capability to be applied to other components, such as brakes and motors. To validate the proposed technique, the necessary algorithm calculations, simulation, and laboratory prototype results are provided. The proposed work is proven accurate with fast response in healthy and faulty conditions.
{"title":"An Adaptive Fault Diagnosis of Electric Vehicles: An Artificial Intelligence Blended Signal Processing Methodology","authors":"Lingli Gong;Anshuman Sharma;Mohammad Abdul Bhuiya;Hilmy Awad;Mohamed Z. Youssef","doi":"10.1109/ICJECE.2023.3264852","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3264852","url":null,"abstract":"This article demonstrates an innovative design of a sensorless technique to diagnose, monitor, and broadcast faults in an electric vehicle’s (EV) propulsion operating conditions. By utilizing the artificial intelligence with a signal processing mixed clustering technique, an onboard health monitoring system (HMS) has been presented. The clustering technique uses a data-mining approach to prevent future failures for predictive maintenance planning, which is novel. For example, the propulsion inverter is equipped with a diagnostic system that uses the proposed algorithm to compare the reference gate-driving signal with the actual output voltage of the voltage source inverter (VSI). This article presents different failure scenarios of the inverter and demonstrates the capability to be applied to other components, such as brakes and motors. To validate the proposed technique, the necessary algorithm calculations, simulation, and laboratory prototype results are provided. The proposed work is proven accurate with fast response in healthy and faulty conditions.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 3","pages":"196-206"},"PeriodicalIF":0.0,"publicationDate":"2023-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68026228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-08DOI: 10.1109/ICJECE.2023.3252088
R. Selvakumar;K. Venkatalakshmi
This article presents a non-convex optimized support vector machine (NCVX OSVM) algorithm for active steering stability of vehicles on a curved road. Initially, we considered a curved road geometrics formulation and designed a time-distributed (TD) model for NCVX OSVM to compute the steering angle 0°–180° at 10 m/s to follow active navigation at the highest curve entry speed. The proposed TD NCVX OSVM is interconnected with three modules. In the first module, formulated NCVX cost functions and Optimized SVM for smooth steering stability. The second module is based on improving faster training time (IFTT) by using the Naive Bayes probabilistic classifier (NBPC). The third module uses an optimized non-convex (NCVX) cost function to reduce the error phenomenon. The performance of these three modules is evaluated by several 100 data points from vehicle onboard sensors. Further, it is pre-processed in the curved road (start, continue, exit) conditions. The decisive of TD-NCVX OSVM design is demonstrated by using experimental learning on FPGA Zynq 7000 processor and programmed with python script. The empirical calculation shows an accuracy of 98.36%. Furthermore, the proposed design predicts an acceptable upper limit for curved steering whenever the vehicle turning speed is greater than 30 mi/h.
{"title":"Time-Distributed Non-Convex Optimized Support Vector Machine for Vehicular Tracking Systems","authors":"R. Selvakumar;K. Venkatalakshmi","doi":"10.1109/ICJECE.2023.3252088","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3252088","url":null,"abstract":"This article presents a non-convex optimized support vector machine (NCVX OSVM) algorithm for active steering stability of vehicles on a curved road. Initially, we considered a curved road geometrics formulation and designed a time-distributed (TD) model for NCVX OSVM to compute the steering angle 0°–180° at 10 m/s to follow active navigation at the highest curve entry speed. The proposed TD NCVX OSVM is interconnected with three modules. In the first module, formulated NCVX cost functions and Optimized SVM for smooth steering stability. The second module is based on improving faster training time (IFTT) by using the Naive Bayes probabilistic classifier (NBPC). The third module uses an optimized non-convex (NCVX) cost function to reduce the error phenomenon. The performance of these three modules is evaluated by several 100 data points from vehicle onboard sensors. Further, it is pre-processed in the curved road (start, continue, exit) conditions. The decisive of TD-NCVX OSVM design is demonstrated by using experimental learning on FPGA Zynq 7000 processor and programmed with python script. The empirical calculation shows an accuracy of 98.36%. Furthermore, the proposed design predicts an acceptable upper limit for curved steering whenever the vehicle turning speed is greater than 30 mi/h.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 2","pages":"170-178"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68017010","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-08DOI: 10.1109/ICJECE.2023.3260830
Dileep Sivaraman;Songpol Ongwattanakul;Jackrit Suthakorn;Branesh M. Pillai
In motion-control applications, noise and dynamic nonlinearities influence the performance of control systems and lead to unpredictable disturbances. The dc servo motors used in motion control applications should have precise control methods to achieve the desired responses. Therefore, predicting and compensating for the disturbance are essential for increasing system robustness and achieving high precision and fast reaction. This article introduces the polynomial predictive filtering (PPF) method to estimate the states of a system using polynomial extrapolation of consecutive and evenly spaced sensor data. Acceleration-/torque-based experiments are conducted to validate the effectiveness and viability of the proposed method. The difference between the real-time sensor data and the PPF-based predicted value shows a standard deviation of less than 0.15 and $1 times 10^{-5}$ for the velocity and disturbance torque, respectively.
{"title":"Nonlinear Dynamic States’ Estimation and Prediction Using Polynomial Predictive Modeling","authors":"Dileep Sivaraman;Songpol Ongwattanakul;Jackrit Suthakorn;Branesh M. Pillai","doi":"10.1109/ICJECE.2023.3260830","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3260830","url":null,"abstract":"In motion-control applications, noise and dynamic nonlinearities influence the performance of control systems and lead to unpredictable disturbances. The dc servo motors used in motion control applications should have precise control methods to achieve the desired responses. Therefore, predicting and compensating for the disturbance are essential for increasing system robustness and achieving high precision and fast reaction. This article introduces the polynomial predictive filtering (PPF) method to estimate the states of a system using polynomial extrapolation of consecutive and evenly spaced sensor data. Acceleration-/torque-based experiments are conducted to validate the effectiveness and viability of the proposed method. The difference between the real-time sensor data and the PPF-based predicted value shows a standard deviation of less than 0.15 and \u0000<inline-formula> <tex-math>$1 times 10^{-5}$ </tex-math></inline-formula>\u0000 for the velocity and disturbance torque, respectively.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 3","pages":"185-195"},"PeriodicalIF":0.0,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68026227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this article, we have proposed an electronic tunable grounded meminductor emulator (MIE) using a single voltage difference transconductance amplifier (VDTA). Along with one VDTA, two MOSFETs and two capacitors are used in the proposed MIE. Overall, the proposed MIE requires only 18 MOS transistors and two grounded capacitors. The performance of the proposed MIE was validated using Cadence Virtuoso with a 180-nm CMOS library. The layout area of the emulator is only 1081 $mu text{m}^{2}$ . The proposed design operates up to 25 MHz. To validate the theoretical and simulation results, an experiment was performed using CA3080 ICs and experimental results validate the simulated result. The power consumption of the proposed design is 5.93 mW.
{"title":"A New Electronic Tunable High-Frequency Meminductor Emulator Based on a Single VDTA","authors":"Pankaj Kumar Sharma;Sadaf Tasneem;Rajeev Kumar Ranjan","doi":"10.1109/ICJECE.2023.3261886","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3261886","url":null,"abstract":"In this article, we have proposed an electronic tunable grounded meminductor emulator (MIE) using a single voltage difference transconductance amplifier (VDTA). Along with one VDTA, two MOSFETs and two capacitors are used in the proposed MIE. Overall, the proposed MIE requires only 18 MOS transistors and two grounded capacitors. The performance of the proposed MIE was validated using Cadence Virtuoso with a 180-nm CMOS library. The layout area of the emulator is only 1081 \u0000<inline-formula> <tex-math>$mu text{m}^{2}$ </tex-math></inline-formula>\u0000. The proposed design operates up to 25 MHz. To validate the theoretical and simulation results, an experiment was performed using CA3080 ICs and experimental results validate the simulated result. The power consumption of the proposed design is 5.93 mW.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 2","pages":"179-184"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68017009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-06-01DOI: 10.1109/ICJECE.2023.3254528
Najah Abuali;Massa Ndong;Mohammad Hayajneh
Space-air-ground integrated network (SAGIN), as a three-tiered architecture that assimilates satellite systems, aerial, and terrestrial communication networks, has become an intensive research domain in the present era of communications. SAGIN-based communication models are developed to enhance the user’s quality of experience (QoE). Besides providing noteworthy benefits in various applications and services, SAGIN has unprecedented challenges because of its self-organized, unpredictable, and heterogeneous nature. Relaying equipment in SAGIN can be a very low-orbit satellite, a base station (BS), and an unmanned vehicle assisting a pair of mobile users’ communications. Thus, developing a robust device-to-device (D2D) direct and relaying communication model concerning channel distribution is crucial. Based on this concern, this article proposes a relay-aided D2D multiple–input and multiple–output (MIMO) scheme (RAS) for enhancing the optimal energy efficiency (EE) as a function of spectral efficiency (SE). The proposed model derives a relay-based amplify-and-forward (AF) MIMO multihop communication system for implementation. The proposed computations of optimal EE and SE for D2D MIMO show that the approximation provided by a random matrix approximation is constrained to a specific signal-to-noise ratio (SNR) range when the optimal SE and EE are derived using Gaussian quadrature and a hypergeometric function.
天-空-地一体化网络（SAGIN）作为一种融合了卫星系统、航空和地面通信网络的三层架构，已成为当今通信领域的一个密集研究领域。基于 SAGIN 的通信模型旨在提高用户的体验质量（QoE）。除了在各种应用和服务中提供显著优势外，SAGIN 还因其自组织、不可预测和异构的特性而面临前所未有的挑战。SAGIN 中的中继设备可以是超低轨道卫星、基站（BS），也可以是协助一对移动用户通信的无人驾驶飞行器。因此，开发一个有关信道分配的稳健的设备对设备（D2D）直接和中继通信模型至关重要。基于这一考虑，本文提出了一种中继辅助 D2D 多入多出（MIMO）方案（RAS），以提高作为频谱效率（SE）函数的最佳能效（EE）。所提出的模型推导了一个基于中继的放大和前向（AF）MIMO 多跳通信系统的实现。拟议的 D2D MIMO 最佳 EE 和 SE 计算表明，当使用高斯正交和超几何函数得出最佳 SE 和 EE 时，随机矩阵近似所提供的近似值受限于特定的信噪比 (SNR) 范围。
{"title":"Relay-Aided D2D MIMO Scheme (RAS) for Achieving Energy Efficiency in Satellite-Air-Ground Integrated Networks (SAGIN) Schéma D2D MIMO assisté par relais (RAS) pour atteindre l’efficacité énergétique dans les réseaux intégrés satellite-air-sol (SAGIN)","authors":"Najah Abuali;Massa Ndong;Mohammad Hayajneh","doi":"10.1109/ICJECE.2023.3254528","DOIUrl":"10.1109/ICJECE.2023.3254528","url":null,"abstract":"Space-air-ground integrated network (SAGIN), as a three-tiered architecture that assimilates satellite systems, aerial, and terrestrial communication networks, has become an intensive research domain in the present era of communications. SAGIN-based communication models are developed to enhance the user’s quality of experience (QoE). Besides providing noteworthy benefits in various applications and services, SAGIN has unprecedented challenges because of its self-organized, unpredictable, and heterogeneous nature. Relaying equipment in SAGIN can be a very low-orbit satellite, a base station (BS), and an unmanned vehicle assisting a pair of mobile users’ communications. Thus, developing a robust device-to-device (D2D) direct and relaying communication model concerning channel distribution is crucial. Based on this concern, this article proposes a relay-aided D2D multiple–input and multiple–output (MIMO) scheme (RAS) for enhancing the optimal energy efficiency (EE) as a function of spectral efficiency (SE). The proposed model derives a relay-based amplify-and-forward (AF) MIMO multihop communication system for implementation. The proposed computations of optimal EE and SE for D2D MIMO show that the approximation provided by a random matrix approximation is constrained to a specific signal-to-noise ratio (SNR) range when the optimal SE and EE are derived using Gaussian quadrature and a hypergeometric function.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 4","pages":"333-341"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80418779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-23DOI: 10.1109/ICJECE.2023.3253547
Dabeeruddin Syed;Ameema Zainab;Shady S. Refaat;Haitham Abu-Rub;Othmane Bouhali;Ali Ghrayeb;Mahdi Houchati;Santiago Bañales
In a real-world scenario of load forecasting, it is crucial to determine the energy consumption in electrical networks. The energy consumption data exhibit high variability between historical data and newly arriving data streams. To keep the forecasting models updated with the current trends, it is important to fine-tune the models in a timely manner. This article proposes a reliable inductive transfer learning (ITL) method, to use the knowledge from existing deep learning (DL) load forecasting models, to innovatively develop highly accurate ITL models at a large number of other distribution nodes reducing model training time. The outlier-insensitive clustering-based technique is adopted to group similar distribution nodes into clusters. ITL is considered in the setting of homogeneous inductive transfer. To solve overfitting that exists with ITL, a novel weight regularized optimization approach is implemented. The proposed novel cross-model methodology is evaluated on a real-world case study of 1000 distribution nodes of an electrical grid for one-day ahead hourly forecasting. Experimental results demonstrate that overfitting and negative learning in ITL can be avoided by the dissociated weight regularization (DWR) optimizer and that the proposed methodology delivers a reduction in training time by almost 85.6% and has no noticeable accuracy losses.
{"title":"Inductive Transfer and Deep Neural Network Learning-Based Cross-Model Method for Short-Term Load Forecasting in Smarts Grids","authors":"Dabeeruddin Syed;Ameema Zainab;Shady S. Refaat;Haitham Abu-Rub;Othmane Bouhali;Ali Ghrayeb;Mahdi Houchati;Santiago Bañales","doi":"10.1109/ICJECE.2023.3253547","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3253547","url":null,"abstract":"In a real-world scenario of load forecasting, it is crucial to determine the energy consumption in electrical networks. The energy consumption data exhibit high variability between historical data and newly arriving data streams. To keep the forecasting models updated with the current trends, it is important to fine-tune the models in a timely manner. This article proposes a reliable inductive transfer learning (ITL) method, to use the knowledge from existing deep learning (DL) load forecasting models, to innovatively develop highly accurate ITL models at a large number of other distribution nodes reducing model training time. The outlier-insensitive clustering-based technique is adopted to group similar distribution nodes into clusters. ITL is considered in the setting of homogeneous inductive transfer. To solve overfitting that exists with ITL, a novel weight regularized optimization approach is implemented. The proposed novel cross-model methodology is evaluated on a real-world case study of 1000 distribution nodes of an electrical grid for one-day ahead hourly forecasting. Experimental results demonstrate that overfitting and negative learning in ITL can be avoided by the dissociated weight regularization (DWR) optimizer and that the proposed methodology delivers a reduction in training time by almost 85.6% and has no noticeable accuracy losses.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 2","pages":"157-169"},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9349829/10137376/10132308.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68016403","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}
Finite control set-model predictive control (FCS–MPC) has a simple and intuitive optimization procedure. Therefore, FCS–MPC is increasingly being applied to control strategies for electrical drive systems. This article presents a method for servo brake control of a permanent magnet synchronous motor (PMSM) based on FCS–MPC. Accordingly, we propose a reference trajectory introduced in a cost function for brake motions and an alternating procedure with speed control. Moreover, this article clarifies the problem peculiar to servo-brake control using FCS–MPC, i.e., the reduction in tracking performance near the brake position because of the low resolution of the output voltage. In addition to the conventional method, a finite number of smoothed voltages were applied as candidate voltages for FCS–MPC to improve the tracking performance near the brake position. The smoothed voltages can effectively increase the resolution of the output voltage, which results in fewer steady-state errors in angular position tracking during servo brake motion. The simulation and experimental results obtained using a PMSM drive system reveal that the proposed strategy exhibited high performance in tracking the reference target during the operation of servobrakes and the ability to seamlessly alternate between servo brake and motor operations.
{"title":"Application of Finite Control Set–Model Predictive Control for Servo Brake Motion in PMSM Drives","authors":"Hiroaki Kawai;Julien Cordier;Ralph Kennel;Shinji Doki","doi":"10.1109/ICJECE.2022.3233029","DOIUrl":"https://doi.org/10.1109/ICJECE.2022.3233029","url":null,"abstract":"Finite control set-model predictive control (FCS–MPC) has a simple and intuitive optimization procedure. Therefore, FCS–MPC is increasingly being applied to control strategies for electrical drive systems. This article presents a method for servo brake control of a permanent magnet synchronous motor (PMSM) based on FCS–MPC. Accordingly, we propose a reference trajectory introduced in a cost function for brake motions and an alternating procedure with speed control. Moreover, this article clarifies the problem peculiar to servo-brake control using FCS–MPC, i.e., the reduction in tracking performance near the brake position because of the low resolution of the output voltage. In addition to the conventional method, a finite number of smoothed voltages were applied as candidate voltages for FCS–MPC to improve the tracking performance near the brake position. The smoothed voltages can effectively increase the resolution of the output voltage, which results in fewer steady-state errors in angular position tracking during servo brake motion. The simulation and experimental results obtained using a PMSM drive system reveal that the proposed strategy exhibited high performance in tracking the reference target during the operation of servobrakes and the ability to seamlessly alternate between servo brake and motor operations.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 2","pages":"117-129"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68014921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-22DOI: 10.1109/ICJECE.2023.3251644
{"title":"IEEE Canadian Journal of Electrical and Computer Engineering","authors":"","doi":"10.1109/ICJECE.2023.3251644","DOIUrl":"https://doi.org/10.1109/ICJECE.2023.3251644","url":null,"abstract":"","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"46 1","pages":"C2-C2"},"PeriodicalIF":0.0,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/9349829/10058052/10078363.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68038577","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}
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