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Faa-Jeng Lin
Department of Electrical Engineering, National Central University, Taoyuan, Taiwan

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Short Biography

He received a Ph.D. degree from National Tsing Hua University, Taiwan, in 1993. He is a Chair Professor at the Department of Electrical Engineering, National Central University, Taiwan. His research interests include AC motor drives, power electronics, renewable energies, smart grids, intelligent and nonlinear control theories. He was Associate Editor of IEEE Trans. Fuzzy Systems; the President, Taiwan Smart Grid Industry Association. He is now Associate Editor of IEEE Trans. Power Electronics and the Executive Director of Taiwan Power Company. He received the Outstanding Research Awards from the National Science Council, Taiwan, in 2004, 2010 and 2013. He is also an IEEE Fellow.

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Article
Published: 05 May 2021 in International Journal of Fuzzy Systems
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A supercapacitor (SC)-based interior permanent magnet synchronous motor (IPMSM) drive including the speed tracking of a specific velocity profile and the charging of the SC is developed in this study to emulate the operation of an urban light rail vehicle (LRV). In the SC-based IPMSM drive, the motoring mode to emulate the LRV speed tracking control and the charging mode for the charging of the SC are both designed. In the motoring mode, a field-oriented controlled (FOC) IPMSM drive system is developed to emulate the speed control of an LRV. In the charging mode, the constant current and constant voltage (CC–CV) charging strategy is developed for the charging of the SC. Moreover, the above two modes use the same inverter and coordinate transformations to reduce the design complexity. Furthermore, in order to test the performance of SC, the speed command of the emulated LRV is obtained using a specific testing driving cycle. The design objective is for fast charging of SC being able to provide enough energy for the emulated LRV to operate a full testing driving cycle. In addition, to improve the transient speed response of the emulated LRV, a Chebyshev fuzzy neural network (CheFNN) intelligent speed controller is proposed. Finally, the simulation and experimental results are given to demonstrate the effectiveness of the developed CC–CV charging strategy for the SC and the proposed CheFNN speed controller for the emulated LRV.

ACS Style

Faa-Jeng Lin; Jen-Chung Liao; En-Wei Chang. A Supercapacitor-Based Interior Permanent Magnet Synchronous Motor Drive Using Intelligent Control for Light Rail Vehicle. International Journal of Fuzzy Systems 2021, 1 -17.

AMA Style

Faa-Jeng Lin, Jen-Chung Liao, En-Wei Chang. A Supercapacitor-Based Interior Permanent Magnet Synchronous Motor Drive Using Intelligent Control for Light Rail Vehicle. International Journal of Fuzzy Systems. 2021; ():1-17.

Chicago/Turabian Style

Faa-Jeng Lin; Jen-Chung Liao; En-Wei Chang. 2021. "A Supercapacitor-Based Interior Permanent Magnet Synchronous Motor Drive Using Intelligent Control for Light Rail Vehicle." International Journal of Fuzzy Systems , no. : 1-17.

Journal article
Published: 04 February 2021 in IEEE/ASME Transactions on Mechatronics
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In order to develop a wide speed range operation of synchronous reluctance motor (SynRM) drive system for constant torque, constant power and reduced power regions, an extended speed control (ESC) with maximum efficiency (ME), flux weakening (FW), and maximum torque per voltage (MTPV) is proposed in this study. Firstly, a conventional FW control system with field-oriented control is introduced. Moreover, to achieve the ME operation, a lookup table of the results of ME analysis by using the finite element analysis technology is adopted to produce the d-axis current commands for the current control mode of the SynRM in the constant torque region. Furthermore, the FW voltage controller is employed to produce the incremental value of the stator voltage command to achieve the maximum voltage limit. In addition, a novel feedforward based voltage angle controller with an MTPV limiter is designed to generate the voltage angle command for the voltage control mode in the constant power and reduced power regions. Finally, the proposed ESC system is implemented in a 32-bit floating-point digital signal processor TMS320F28075 and its robustness and effectiveness are verified by some experimental results.

ACS Style

Shih-Gang Chen; Faa-Jeng Lin; Chia-Hui Liang; Chen-Hao Liao. Development of FW and MTPV Control for SynRM via Feedforward Voltage Angle Control. IEEE/ASME Transactions on Mechatronics 2021, PP, 1 -1.

AMA Style

Shih-Gang Chen, Faa-Jeng Lin, Chia-Hui Liang, Chen-Hao Liao. Development of FW and MTPV Control for SynRM via Feedforward Voltage Angle Control. IEEE/ASME Transactions on Mechatronics. 2021; PP (99):1-1.

Chicago/Turabian Style

Shih-Gang Chen; Faa-Jeng Lin; Chia-Hui Liang; Chen-Hao Liao. 2021. "Development of FW and MTPV Control for SynRM via Feedforward Voltage Angle Control." IEEE/ASME Transactions on Mechatronics PP, no. 99: 1-1.

Journal article
Published: 14 January 2021 in IEEE Transactions on Industrial Electronics
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In order to design a high-performance synchronous reluctance motor (SynRM) drive system, a novel adaptive complementary sliding mode (ACSM) speed control and an effective d-axis current control (EDCC) are proposed in this study. Firstly, a classical proportional-integral based field-oriented control of the SynRM drive with a constant d-axis current command is introduced. However, the constant d-axis current command is obtained by trial and error method or satisfying the minimum excitation current of the SynRM. More importantly, the constant value of the d-axis current command is not suitable for the highly nonlinear and time-varying SynRM drive at the varied load torque conditions. Therefore, an ACSM speed control with the d-axis current control (ACSMSC-DCC) system is designed for the speed regulation of the SynRM. The ACSM speed control is proposed to generate the q-axis current commands, and an EDCC by using the stator flux estimator is proposed to produce the d-axis current commands. Finally, the proposed ACSMSC-DCC system is implemented in a 32-bit floating-point digital signal processor TMS320F28075 and its effectiveness are verified by some experimental results.

ACS Style

Faa-Jeng Lin; Shih-Gang Chen; Ming-Shi Huang; Chia-Hui Liang; Chen-Hao Liao. Adaptive Complementary Sliding Mode Control for Synchronous Reluctance Motor with Direct-Axis Current Control. IEEE Transactions on Industrial Electronics 2021, PP, 1 -1.

AMA Style

Faa-Jeng Lin, Shih-Gang Chen, Ming-Shi Huang, Chia-Hui Liang, Chen-Hao Liao. Adaptive Complementary Sliding Mode Control for Synchronous Reluctance Motor with Direct-Axis Current Control. IEEE Transactions on Industrial Electronics. 2021; PP (99):1-1.

Chicago/Turabian Style

Faa-Jeng Lin; Shih-Gang Chen; Ming-Shi Huang; Chia-Hui Liang; Chen-Hao Liao. 2021. "Adaptive Complementary Sliding Mode Control for Synchronous Reluctance Motor with Direct-Axis Current Control." IEEE Transactions on Industrial Electronics PP, no. 99: 1-1.

Journal article
Published: 09 January 2021 in Energies
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An interior permanent magnet synchronous motor (IPMSM) drive system with machine learning-based maximum torque per ampere (MTPA) as well as flux-weakening (FW) control was developed and is presented in this study. Since the control performance of IPMSM varies significantly due to the temperature variation and magnetic saturation, a machine learning-based MTPA control using a Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) was developed. First, the d-axis current command, which can achieve the MTPA control of the IPMSM, is derived. Then, the difference value of the dq-axis inductance of the IPMSM is obtained by the PPFNN-AMF and substituted into the d-axis current command of the MTPA to alleviate the saturation effect in the constant torque region. Moreover, a voltage control loop, which can limit the inverter output voltage to the maximum output voltage of the inverter at high-speed, is designed for the FW control in the constant power region. In addition, an adaptive complementary sliding mode (ACSM) speed controller is developed to improve the transient response of the speed control. Finally, some experimental results are given to demonstrate the validity of the proposed high-performance control strategies.

ACS Style

Faa-Jeng Lin; Yi-Hung Liao; Jyun-Ru Lin; Wei-Ting Lin. Interior Permanent Magnet Synchronous Motor Drive System with Machine Learning-Based Maximum Torque per Ampere and Flux-Weakening Control. Energies 2021, 14, 346 .

AMA Style

Faa-Jeng Lin, Yi-Hung Liao, Jyun-Ru Lin, Wei-Ting Lin. Interior Permanent Magnet Synchronous Motor Drive System with Machine Learning-Based Maximum Torque per Ampere and Flux-Weakening Control. Energies. 2021; 14 (2):346.

Chicago/Turabian Style

Faa-Jeng Lin; Yi-Hung Liao; Jyun-Ru Lin; Wei-Ting Lin. 2021. "Interior Permanent Magnet Synchronous Motor Drive System with Machine Learning-Based Maximum Torque per Ampere and Flux-Weakening Control." Energies 14, no. 2: 346.

Journal article
Published: 14 August 2020 in IEEE Transactions on Power Electronics
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To develop a high-performance synchronous reluctance motor (SynRM) drive system, a novel maximum power factor control (MPFC) using a current angle controller with stator resistance and stator flux estimators is proposed. Firstly, a traditional maximum power factor control (TMPFC) system using a saliency ratio of SynRM to generate a fixed current angle command is described. Since the saliency ratio requires offline pre-preparation and can't be adjusted automatically, it is difficult to improve the performance of MPFC in different operating regions because of the increasing of manufacturing cost and time-consuming. Therefore, an intelligent-maximum power factor searching control (MPFSC) using a recurrent Chebyshev fuzzy neural network (RCFNN) current angle controller is developed. To search the online optimal power factor (PF) points of the SynRM under different operating conditions, the RCFNN current angle controller is designed to produce compensated current angle commands. Moreover, a proportional-integral speed controller is adopted to generate the stator current magnitude command, and the proposed intelligent-MPFSC is employed to generate the current angle command. Furthermore, the proposed intelligent-MPFSC system is implemented in a digital signal processor. Finally, the current angle commands of the optimal PF can be effectively obtained online at different speed operating commands with varied load torque.

ACS Style

Shih-Gang Chen; Faa-Jeng Lin; Chia-Hui Liang; Chen-Hao Liao. Intelligent Maximum Power Factor Searching Control Using Recurrent Chebyshev Fuzzy Neural Network Current Angle Controller for SynRM Drive System. IEEE Transactions on Power Electronics 2020, 36, 3496 -3511.

AMA Style

Shih-Gang Chen, Faa-Jeng Lin, Chia-Hui Liang, Chen-Hao Liao. Intelligent Maximum Power Factor Searching Control Using Recurrent Chebyshev Fuzzy Neural Network Current Angle Controller for SynRM Drive System. IEEE Transactions on Power Electronics. 2020; 36 (3):3496-3511.

Chicago/Turabian Style

Shih-Gang Chen; Faa-Jeng Lin; Chia-Hui Liang; Chen-Hao Liao. 2020. "Intelligent Maximum Power Factor Searching Control Using Recurrent Chebyshev Fuzzy Neural Network Current Angle Controller for SynRM Drive System." IEEE Transactions on Power Electronics 36, no. 3: 3496-3511.

Journal article
Published: 08 June 2020 in IEEE Access
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A wireless power transfer (WPT) system with bidirectional power flow control for charging batteries and drawing electric power from batteries is presented in this paper for e-bike sharing applications. The proposed WPT consists of a bidirectional DC-DC converter and a sandwiched coil set, which includes primary winding with ferrite pad in charging stations and a movable secondary coil without ferrite pad, which is inserted into the primary coils to receive or transmit energy, on the e-bike. Hence, the proposed coil set could provide high coupling factor, better tolerance of misalignment, and a compact size that enables easy installation in the current mechanism of bike-sharing systems. The Maxwell 3D is adopted to design coil sets, which are based on the known resistance of Litz wire, and the effect of the ferrite pad and misalignment are analyzed to achieve good performance. To select a suitable converter, the characteristics of predesigned coils are simulated using Maxwell 3D, MATLAB and PSIM. Then, a full-bridge CLLC converter with the same resonant frequency for charging and discharging modes is chosen to provide minimal loss by using the synchronous rectifier on the 48-V side and voltage gain without the loading effect. Finally, a digital signal processor-based digitally controlled CLLC converter is constructed to verify the performance of the proposed WPT, which can provide 200 to 48 V/500 W charging and discharging functions with constant current/constant voltage modes and fast-response current control. In addition, the maximum efficiency is 96% at 52 V and 75% rated load in charging mode.

ACS Style

Chih-Chia Liao; Ming-Shi Huang; Zheng-Feng Li; Faa-Jeng Lin; Wei-Ting Wu. Simulation-Assisted Design of a Bidirectional Wireless Power Transfer With Circular Sandwich Coils for E-Bike Sharing System. IEEE Access 2020, 8, 110003 -110017.

AMA Style

Chih-Chia Liao, Ming-Shi Huang, Zheng-Feng Li, Faa-Jeng Lin, Wei-Ting Wu. Simulation-Assisted Design of a Bidirectional Wireless Power Transfer With Circular Sandwich Coils for E-Bike Sharing System. IEEE Access. 2020; 8 ():110003-110017.

Chicago/Turabian Style

Chih-Chia Liao; Ming-Shi Huang; Zheng-Feng Li; Faa-Jeng Lin; Wei-Ting Wu. 2020. "Simulation-Assisted Design of a Bidirectional Wireless Power Transfer With Circular Sandwich Coils for E-Bike Sharing System." IEEE Access 8, no. : 110003-110017.

Journal article
Published: 01 April 2020 in IEEE Access
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An intelligent control method using recurrent wavelet fuzzy neural network (RWFNN) is proposed to improve the low-voltage ride through (LVRT) performance of a two-stage photovoltaic (PV) power plant under grid faults for the weak grid conditions. The PV power plant comprises an interleaved DC/DC converter and a three-level neutral-point clamped (NPC) smart inverter, in which the output active and reactive powers of the inverter can be predetermined in accordance with grid codes of the utilities. Moreover, for the purpose of improving the control performance of the PV power plant to handle the grid faults for the weak grid conditions, a new RWFNN with online training is proposed to replace the traditional proportional-integral (PI) controller for the active and reactive powers control of the smart inverter. Furthermore, the proposed controllers are implemented by two floating-point digital signal processors (DSPs). From the simulation and experimental results, excellent control performance for the tracking of active and reactive powers under grid faults for the weak grid conditions can be achieved by using the proposed intelligent control method.

ACS Style

Faa-Jeng Lin; Kuang-Hsiung Tan; Wen-Chou Luo; Guo-Deng Xiao. Improved LVRT Performance of PV Power Plant Using Recurrent Wavelet Fuzzy Neural Network Control for Weak Grid Conditions. IEEE Access 2020, 8, 69346 -69358.

AMA Style

Faa-Jeng Lin, Kuang-Hsiung Tan, Wen-Chou Luo, Guo-Deng Xiao. Improved LVRT Performance of PV Power Plant Using Recurrent Wavelet Fuzzy Neural Network Control for Weak Grid Conditions. IEEE Access. 2020; 8 (99):69346-69358.

Chicago/Turabian Style

Faa-Jeng Lin; Kuang-Hsiung Tan; Wen-Chou Luo; Guo-Deng Xiao. 2020. "Improved LVRT Performance of PV Power Plant Using Recurrent Wavelet Fuzzy Neural Network Control for Weak Grid Conditions." IEEE Access 8, no. 99: 69346-69358.

Journal article
Published: 09 January 2020 in IEEE Transactions on Industrial Informatics
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A real-time moment of inertia identification technique using Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) for an interior permanent magnet synchronous motor (IPMSM) servo drive is proposed in this study. The estimated moment of inertia will be used in the online design of an integral-proportional (IP) speed controller to achieve the gains auto-tuning of the IPMSM servo drive. In the proposed method, the dynamic analysis of a field-oriented control (FOC) IPMSM servo drive system with an IP speed controller is constructed first. Then, a heuristic approach using the PPFNN-AMF is proposed for the real-time identification of the moment of inertia of the IPMSM servo drive system. Moreover, the network structure and the convergence analysis of the PPFNN-AMF are devised and derivated. Furthermore, an IPMSM servo drive based on a high performance digital signal processor (DSP) is developed. Finally, from the experimental results, the gains of the IP speed controller can be tuned online effectively at different operating conditions with robust control characteristics.

ACS Style

Faa-Jeng Lin; Shih-Gang Chen; Shuai Li; Hsiao-Tse Chou; Jyun-Ru Lin. Online Autotuning Technique for IPMSM Servo Drive by Intelligent Identification of Moment of Inertia. IEEE Transactions on Industrial Informatics 2020, 16, 7579 -7590.

AMA Style

Faa-Jeng Lin, Shih-Gang Chen, Shuai Li, Hsiao-Tse Chou, Jyun-Ru Lin. Online Autotuning Technique for IPMSM Servo Drive by Intelligent Identification of Moment of Inertia. IEEE Transactions on Industrial Informatics. 2020; 16 (12):7579-7590.

Chicago/Turabian Style

Faa-Jeng Lin; Shih-Gang Chen; Shuai Li; Hsiao-Tse Chou; Jyun-Ru Lin. 2020. "Online Autotuning Technique for IPMSM Servo Drive by Intelligent Identification of Moment of Inertia." IEEE Transactions on Industrial Informatics 16, no. 12: 7579-7590.

Journal article
Published: 20 November 2019 in IEEE Transactions on Power Electronics
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The design and analysis of a novel current angle-based adaptive backstepping (ABS) speed control system for a synchronous reluctance motor (SynRM) drive system is presented in this study. First, a proportional-integral (PI) control system with field-oriented control (FOC) is described. Owing to the unmodeled dynamics and magnetic saturation effects of the SynRM, currently there is no predominant way to design the command of d-axis current for the SynRM. Therefore, an ABS based on current angle control (ABS-CAC) system is designed for the speed tracking of SynRM. The ABS speed tracking control is proposed to generate the stator current command, and a lookup table (LUT) of the results of maximum torque per ampere (MTPA) analysis by using the finite element analysis (FEA) method is proposed to provide the current angle commands. Moreover, to improve the transient dynamic response of SynRM under MTPA operating conditions, an intelligent speed transient control (ISTC) system using a recurrent Hermite fuzzy neural network (RHFNN) is developed to generate the compensated current angle command. The proposed intelligent ABS-CAC is implemented in a 32-bit floating-point digital signal processor (DSP) TMS320F28075. Finally, some experimental results are provided to demonstrate the robustness and effectiveness of the proposed control system.

ACS Style

Faa-Jeng Lin; Ming-Shi Huang; Shih-Gang Chen; Che-Wei Hsu; Chia-Hui Liang. Adaptive Backstepping Control for Synchronous Reluctance Motor Based on Intelligent Current Angle Control. IEEE Transactions on Power Electronics 2019, 35, 7465 -7479.

AMA Style

Faa-Jeng Lin, Ming-Shi Huang, Shih-Gang Chen, Che-Wei Hsu, Chia-Hui Liang. Adaptive Backstepping Control for Synchronous Reluctance Motor Based on Intelligent Current Angle Control. IEEE Transactions on Power Electronics. 2019; 35 (7):7465-7479.

Chicago/Turabian Style

Faa-Jeng Lin; Ming-Shi Huang; Shih-Gang Chen; Che-Wei Hsu; Chia-Hui Liang. 2019. "Adaptive Backstepping Control for Synchronous Reluctance Motor Based on Intelligent Current Angle Control." IEEE Transactions on Power Electronics 35, no. 7: 7465-7479.

Journal article
Published: 20 November 2019 in IEEE Transactions on Power Electronics
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A microgrid with virtual inertia using master-slave control is proposed in this study to overcome the drawbacks of traditional inverter-based distributed generators for lack of inertia and without grid-forming capability. The microgrid using master-slave control is composed of a storage system, a photovoltaic (PV) system and a varying resistive three-phase load. The storage system and PV system are regarded as the master unit and the slave unit respectively in the microgrid. Moreover, in order to improve the reactive power control in grid-connected mode and the transient response of microgrid during the switching between the grid-connected mode and islanding mode, an online trained recurrent probabilistic wavelet fuzzy neural network (RPWFNN) is proposed to replace the conventional proportional-integral (PI) controller in the storage system. Furthermore, when the microgrid is operated in islanding mode, the load variation will have serious influence on the voltage control of the microgrid. Thus, the RPWFNN control is also proposed to improve the transient and steady-state responses of voltage control in the microgrid. Finally, according to some experimental results, excellent control performance of the microgrid with virtual inertia using the proposed intelligent controller can be achieved.

ACS Style

Kuang-Hsiung Tan; Faa-Jeng Lin; Cheng-Ming Shih; Che-Nan Kuo. Intelligent Control of Microgrid With Virtual Inertia Using Recurrent Probabilistic Wavelet Fuzzy Neural Network. IEEE Transactions on Power Electronics 2019, 35, 7451 -7464.

AMA Style

Kuang-Hsiung Tan, Faa-Jeng Lin, Cheng-Ming Shih, Che-Nan Kuo. Intelligent Control of Microgrid With Virtual Inertia Using Recurrent Probabilistic Wavelet Fuzzy Neural Network. IEEE Transactions on Power Electronics. 2019; 35 (7):7451-7464.

Chicago/Turabian Style

Kuang-Hsiung Tan; Faa-Jeng Lin; Cheng-Ming Shih; Che-Nan Kuo. 2019. "Intelligent Control of Microgrid With Virtual Inertia Using Recurrent Probabilistic Wavelet Fuzzy Neural Network." IEEE Transactions on Power Electronics 35, no. 7: 7451-7464.

Journal article
Published: 08 August 2019 in IEEE Transactions on Cybernetics
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Parallel robots are usually required to perform real-time tracking control tasks in the presence of external disturbances in the complex environment. Conventional zeroing neural-dynamics (ZNDs) provide an alternative solution for the real-time tracking control of parallel robots due to its capacity of parallel processing and nonlinearity handling. However, it is still a challenge for the solution in a unified framework of the ZND to deal with the external disturbances, and simultaneously possess a finite-time convergence property. In this paper, a novel ZND model by exploring the super-twisting (ST) algorithm, named ST-ZND model, is proposed. The theoretical analyses on the global stability, finite-time convergence, as well as the robustness against the external disturbances are rigorously presented. Finally, the effectiveness and superiority of the ST-ZND model for the real-time tracking control of parallel robots are demonstrated by two illustrative examples, comparisons, and convergence tests.

ACS Style

Dechao Chen; Shuai Li; Faa-Jeng Lin; Qing Wu. New Super-Twisting Zeroing Neural-Dynamics Model for Tracking Control of Parallel Robots: A Finite-Time and Robust Solution. IEEE Transactions on Cybernetics 2019, 50, 2651 -2660.

AMA Style

Dechao Chen, Shuai Li, Faa-Jeng Lin, Qing Wu. New Super-Twisting Zeroing Neural-Dynamics Model for Tracking Control of Parallel Robots: A Finite-Time and Robust Solution. IEEE Transactions on Cybernetics. 2019; 50 (6):2651-2660.

Chicago/Turabian Style

Dechao Chen; Shuai Li; Faa-Jeng Lin; Qing Wu. 2019. "New Super-Twisting Zeroing Neural-Dynamics Model for Tracking Control of Parallel Robots: A Finite-Time and Robust Solution." IEEE Transactions on Cybernetics 50, no. 6: 2651-2660.

Journal article
Published: 21 March 2019 in IEEE Transactions on Power Electronics
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In order to construct a high-performance synchronous reluctance motor (SynRM) drive system, an intelligent maximum torque per ampere (MTPA) tracking control using a recurrent Legendre fuzzy neural network (RLFNN) is proposed in this study. First, a traditional MTPA (TMTPA) control system based on fieldoriented control (FOC) is introduced. Since the reluctance torque of the SynRM is highly nonlinear and time-varying, the MTPA tracking control is very difficult to achieve by using the TMTPA control in practical applications. Then, an adaptive computed current (ACC) speed control using the proposed RLFNN for the MTPA tracking control of a SynRM drive system, which does not use a lookup table and can effectively obtain the optimal current angle command of MTPA online, is described in detail. The ACC speed control is applied to generate the stator current magnitude command, and an adaptation law is proposed to online adapt the value of a lumped uncertainty in the ACC control. Moreover, the adaptation law is derived using the Lyapunov stability theorem to guarantee the asymptotic stability of the ACC speed control. Furthermore, the proposed RLFNN is employed to produce the incremental command of the current angle. In addition, the ACC speed control and RLFNN are implemented in a TMS320F28075 32-bit floating-point digital signal processor (DSP) for a 4 kW SynRM drive system. Finally, the robustness and effectiveness of the proposed intelligent MTPA tracking control are verified by some experimental results.

ACS Style

Faa-Jeng Lin; Ming-Shi Huang; Shih-Gang Chen; Che-Wei Hsu. Intelligent Maximum Torque per Ampere Tracking Control of Synchronous Reluctance Motor Using Recurrent Legendre Fuzzy Neural Network. IEEE Transactions on Power Electronics 2019, 34, 12080 -12094.

AMA Style

Faa-Jeng Lin, Ming-Shi Huang, Shih-Gang Chen, Che-Wei Hsu. Intelligent Maximum Torque per Ampere Tracking Control of Synchronous Reluctance Motor Using Recurrent Legendre Fuzzy Neural Network. IEEE Transactions on Power Electronics. 2019; 34 (12):12080-12094.

Chicago/Turabian Style

Faa-Jeng Lin; Ming-Shi Huang; Shih-Gang Chen; Che-Wei Hsu. 2019. "Intelligent Maximum Torque per Ampere Tracking Control of Synchronous Reluctance Motor Using Recurrent Legendre Fuzzy Neural Network." IEEE Transactions on Power Electronics 34, no. 12: 12080-12094.

Journal article
Published: 19 December 2018 in IEEE Transactions on Power Electronics
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ACS Style

Faa-Jeng Lin; Kuang-Hsiung Tan; Yu-Kai Lai; Wen-Chou Luo. Intelligent PV Power System With Unbalanced Current Compensation Using CFNN-AMF. IEEE Transactions on Power Electronics 2018, 34, 8588 -8598.

AMA Style

Faa-Jeng Lin, Kuang-Hsiung Tan, Yu-Kai Lai, Wen-Chou Luo. Intelligent PV Power System With Unbalanced Current Compensation Using CFNN-AMF. IEEE Transactions on Power Electronics. 2018; 34 (9):8588-8598.

Chicago/Turabian Style

Faa-Jeng Lin; Kuang-Hsiung Tan; Yu-Kai Lai; Wen-Chou Luo. 2018. "Intelligent PV Power System With Unbalanced Current Compensation Using CFNN-AMF." IEEE Transactions on Power Electronics 34, no. 9: 8588-8598.

Journal article
Published: 17 October 2018 in IEEE Systems, Man, and Cybernetics Magazine
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To achieve the optimal bandwidth at different speed commands, an online autotuning technique using two two-input, one-output wavelet fuzzy neural networks (WFNNs) for interior permanent magnet synchronous motor (IPMSM) servo drives is proposed in this article. First, the dynamic performance analysis of a field-oriented control (FOC) IPMSM servo drive system is studied. Then, two two-input, one-output, four-layer WFNNs are proposed for the online autotuning of the gains of a proportional?integral (PI) speed controller of the servo motor drive to search for the optimal bandwidth without using the information of plant parameters and the characteristics of the servo motor drive.

ACS Style

Faa-Jeng Lin; Shih-Gang Chen; Wei-An Yu; Hsiao-Tse Chou. Online Autotuning of a Servo Drive: Using Wavelet Fuzzy Neural Networks to Search for the Optimal Bandwidth. IEEE Systems, Man, and Cybernetics Magazine 2018, 4, 28 -37.

AMA Style

Faa-Jeng Lin, Shih-Gang Chen, Wei-An Yu, Hsiao-Tse Chou. Online Autotuning of a Servo Drive: Using Wavelet Fuzzy Neural Networks to Search for the Optimal Bandwidth. IEEE Systems, Man, and Cybernetics Magazine. 2018; 4 (4):28-37.

Chicago/Turabian Style

Faa-Jeng Lin; Shih-Gang Chen; Wei-An Yu; Hsiao-Tse Chou. 2018. "Online Autotuning of a Servo Drive: Using Wavelet Fuzzy Neural Networks to Search for the Optimal Bandwidth." IEEE Systems, Man, and Cybernetics Magazine 4, no. 4: 28-37.

Journal article
Published: 24 August 2018 in IEEE Transactions on Industrial Informatics
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Zeroing neural dynamics (ZND) can be seen as an effective controller to solve various challenging scientific and engineering problems. Computing Lyapunov equation is a kind of important issue in nonlinear systems for stability analysis in control. This paper presents a systematic and constructive procedure on using ZND to design control laws based on the efficient solution of dynamic Lyapunov equation. We particularly address three important aspects in the design: 1. the global stability of ZND, to guarantee the effectiveness of the solution; 2. the robustness against additive noises, to ensure the capability of ZND for using in harsh environments; 3. the finite-time convergence of ZND, to endow ZND for real-time solution of dynamical problems. To do so, a novel formula is first designed in a unified manner of ZND. Differing from the conventional formula appeared in ZND, the proposed formula simultaneously has finite-time convergence and noise robustness property. According to this novel formula, a novel control law (termed nonlinear neural dynamics, NND) is established to compute dynamic Lyapunov equation in the presence of various additive noises. Both theoretical and simulative results ensure the finite-time convergence and noise robustness property of the NND model for computing dynamic Lyapunov equation in the front of various additive noises. As compared to the conventional ZND model for computing dynamic Lyapunov, the superior property of the NND model is further demonstrated.

ACS Style

Lin Xiao; Shuai Li; Faa-Jeng Lin; Zhiguo Tan; Ameer Hamza Khan. Zeroing Neural Dynamics for Control Design: Comprehensive Analysis on Stability, Robustness, and Convergence Speed. IEEE Transactions on Industrial Informatics 2018, 15, 2605 -2616.

AMA Style

Lin Xiao, Shuai Li, Faa-Jeng Lin, Zhiguo Tan, Ameer Hamza Khan. Zeroing Neural Dynamics for Control Design: Comprehensive Analysis on Stability, Robustness, and Convergence Speed. IEEE Transactions on Industrial Informatics. 2018; 15 (5):2605-2616.

Chicago/Turabian Style

Lin Xiao; Shuai Li; Faa-Jeng Lin; Zhiguo Tan; Ameer Hamza Khan. 2018. "Zeroing Neural Dynamics for Control Design: Comprehensive Analysis on Stability, Robustness, and Convergence Speed." IEEE Transactions on Industrial Informatics 15, no. 5: 2605-2616.

Journal article
Published: 01 August 2018 in Energies
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A distribution static compensator (DSTATCOM) is proposed in this study to improve the power quality, which includes the total harmonic distortion (THD) of the grid current and power factor (PF), of a mini grid with nonlinear and linear inductive loads. Moreover, the DC-link voltage regulation control of the DSTATCOM is essential especially under load variation conditions. Therefore, to improve the power quality and keep the DC-link voltage of the DSTATCOM constant under the variation of nonlinear and linear loads effectively, the traditional proportional-integral (PI) controller is substituted with a new online trained compensatory fuzzy neural network with an asymmetric membership function (CFNN-AMF) controller. In the proposed CFNN-AMF, the compensatory parameter to integrate pessimistic and optimistic operations of fuzzy systems is embedded in the CFNN. Furthermore, the dimensions of the Gaussian membership functions are directly extended to AMFs for the optimization of the fuzzy rules and the upgrade of learning ability of the networks. In addition, the network structure and online learning algorithm of the proposed CFNN-AMF are introduced in detail. Finally, the effectiveness and feasibility of the DSTATCOM using the proposed CFNN-AMF controller to improve the power quality and maintain the constant DC-link voltage under the change of nonlinear and linear inductive loads have been demonstrated by some experimental results.

ACS Style

Kuang-Hsiung Tan; Faa-Jeng Lin; Chao-Yang Tsai; Yung-Ruei Chang. A Distribution Static Compensator Using a CFNN-AMF Controller for Power Quality Improvement and DC-Link Voltage Regulation. Energies 2018, 11, 1996 .

AMA Style

Kuang-Hsiung Tan, Faa-Jeng Lin, Chao-Yang Tsai, Yung-Ruei Chang. A Distribution Static Compensator Using a CFNN-AMF Controller for Power Quality Improvement and DC-Link Voltage Regulation. Energies. 2018; 11 (8):1996.

Chicago/Turabian Style

Kuang-Hsiung Tan; Faa-Jeng Lin; Chao-Yang Tsai; Yung-Ruei Chang. 2018. "A Distribution Static Compensator Using a CFNN-AMF Controller for Power Quality Improvement and DC-Link Voltage Regulation." Energies 11, no. 8: 1996.

Journal article
Published: 23 July 2018 in IEEE Transactions on Fuzzy Systems
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The synchronous reluctance motor (SynRM) is a high efficiency and low cost motor with rugged structure. However, the nonlinear and time-varying control characteristics of the SynRM with high torque ripple confine the high-performance applications of this motor. Therefore, an intelligent backstepping control using recurrent feature selection fuzzy neural network (IBSCRFSFNN) is proposed in this study to construct a high-performance SynRM position servo drive system. Firstly, the dynamic model of a vector control SynRM position servo drive is described. Secondly, a backstepping control (BSC) system is designed for the tracking of the position reference. Since the lumped uncertainty of the SynRM position servo drive system is unavailable to obtain in advance, it is very difficult to design an effective BSC in practical applications. Moreover, the sign function in BSC will cause undesired chattering phenomenon which will excite unknown dynamics and wear the ball bearing of the SynRM. To alleviate the existed difficulties in the BSC, the recurrent feature selection fuzzy neural network (RFSFNN) is proposed in this study to approximate an idea BSC. In addition, to compensate the possible approximated error of the RFSFNN, an improved adaptive compensator is augmented. The online learning algorithms of the RFSFNN are derived by using the Lyapunov stability method to assure asymptotical stability. Finally, the proposed control system is implemented in a 32-bit floating-point digital signal processor (DSP). The effectiveness and robustness of the proposed intelligent backstepping control system are verified by some experimental results.

ACS Style

Faa-Jeng Lin; Shih-Gang Chen; Che-Wei Hsu. Intelligent Backstepping Control Using Recurrent Feature Selection Fuzzy Neural Network for Synchronous Reluctance Motor Position Servo Drive System. IEEE Transactions on Fuzzy Systems 2018, 27, 413 -427.

AMA Style

Faa-Jeng Lin, Shih-Gang Chen, Che-Wei Hsu. Intelligent Backstepping Control Using Recurrent Feature Selection Fuzzy Neural Network for Synchronous Reluctance Motor Position Servo Drive System. IEEE Transactions on Fuzzy Systems. 2018; 27 (3):413-427.

Chicago/Turabian Style

Faa-Jeng Lin; Shih-Gang Chen; Che-Wei Hsu. 2018. "Intelligent Backstepping Control Using Recurrent Feature Selection Fuzzy Neural Network for Synchronous Reluctance Motor Position Servo Drive System." IEEE Transactions on Fuzzy Systems 27, no. 3: 413-427.

Research article
Published: 25 January 2018 in Transactions of the Institute of Measurement and Control
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A novel maximum torque per ampere (MTPA) method based on power perturbation for a field-oriented control (FOC) interior permanent magnet synchronous motor (IPMSM) drive system is proposed in this study. The proposed MTPA method is designed based on the power perturbation resulting from the signal injection in the current angle. Moreover, the influence of current and voltage harmonics to the MTPA control can be effectively eliminated. Furthermore, to enhance the robustness of the control system, a real-time design scheme for the integral–proportional (IP) speed controller using a recursive least square (RLS) estimator with disturbance torque feedforward control is developed. The disturbance torque is obtained from an improved disturbance torque observer with online parameters updated. Finally, some experimental results using an IPMSM drive system based on a low-price digital signal processor (DSP) are presented. From the experimental results, the proposed control approach can guarantee the control performance of a speed loop even under a cyclic fluctuating load.

ACS Style

Faa-Jeng Lin; Shih-Gang Chen; Ying-Tsen Liu. A power perturbation-based MTPA control with disturbance torque observer for IPMSM drive system. Transactions of the Institute of Measurement and Control 2018, 40, 3179 -3188.

AMA Style

Faa-Jeng Lin, Shih-Gang Chen, Ying-Tsen Liu. A power perturbation-based MTPA control with disturbance torque observer for IPMSM drive system. Transactions of the Institute of Measurement and Control. 2018; 40 (10):3179-3188.

Chicago/Turabian Style

Faa-Jeng Lin; Shih-Gang Chen; Ying-Tsen Liu. 2018. "A power perturbation-based MTPA control with disturbance torque observer for IPMSM drive system." Transactions of the Institute of Measurement and Control 40, no. 10: 3179-3188.

Journal article
Published: 01 December 2017 in Energies
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A three-phase four-leg inverter-based shunt active power filter (APF) is proposed to compensate three-phase unbalanced currents under unbalanced load conditions in grid-connected operation in this study. Since a DC-link capacitor is required on the DC side of the APF to release or absorb the instantaneous apparent power, the regulation control of the DC-link voltage of the APF is important especially under load variation. In order to improve the regulation control of the DC-link voltage of the shunt APF under variation of three-phase unbalanced load and to compensate the three-phase unbalanced currents effectively, a novel Petri probabilistic fuzzy neural network (PPFNN) controller is proposed to replace the traditional proportional-integral (PI) controller in this study. Furthermore, the network structure and online learning algorithms of the proposed PPFNN are represented in detail. Finally, the effectiveness of the three-phase four-leg inverter-based shunt APF with the proposed PPFNN controller for the regulation of the DC-link voltage and compensation of the three-phase unbalanced current has been demonstrated by some experimental results.

ACS Style

Kuang-Hsiung Tan; Faa-Jeng Lin; Jun-Hao Chen. A Three-Phase Four-Leg Inverter-Based Active Power Filter for Unbalanced Current Compensation Using a Petri Probabilistic Fuzzy Neural Network. Energies 2017, 10, 2005 .

AMA Style

Kuang-Hsiung Tan, Faa-Jeng Lin, Jun-Hao Chen. A Three-Phase Four-Leg Inverter-Based Active Power Filter for Unbalanced Current Compensation Using a Petri Probabilistic Fuzzy Neural Network. Energies. 2017; 10 (12):2005.

Chicago/Turabian Style

Kuang-Hsiung Tan; Faa-Jeng Lin; Jun-Hao Chen. 2017. "A Three-Phase Four-Leg Inverter-Based Active Power Filter for Unbalanced Current Compensation Using a Petri Probabilistic Fuzzy Neural Network." Energies 10, no. 12: 2005.

Journal article
Published: 21 August 2017 in IEEE Transactions on Power Electronics
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This paper presents a novel control method for multimodule photovoltaic microinverter (MI). The proposed MI employs a two-stage topology with active-clamped current-fed push-pull converter cascaded with a full-bridge inverter. This system can operate in grid-connected mode to feed power to the grid with a programmable power factor. This system can also operate in line-interactive mode, i.e., share load power without feeding power to the grid. In the event of grid power failure, the MI can operate in a standalone mode to supply uninterruptible power to the load. This paper presents a multiloop control scheme with power programmable capability for achieving the above multiple functions. In addition, the proposed control scheme embedded a multimodule parallel capability that multiple MI modules can be paralleled to enlarge the capacity with autonomous control in all operation modes. Finally, three 250-W MI modules are adopted to demonstrate the effectiveness of the proposed control method in simulations as well as experiments.

ACS Style

Hsuang-Chang Chiang; Faa-Jeng Lin; Jin-Kuan Chang. Novel Control Method for Multimodule PV Microinverter With Multiple Functions. IEEE Transactions on Power Electronics 2017, 33, 5869 -5879.

AMA Style

Hsuang-Chang Chiang, Faa-Jeng Lin, Jin-Kuan Chang. Novel Control Method for Multimodule PV Microinverter With Multiple Functions. IEEE Transactions on Power Electronics. 2017; 33 (7):5869-5879.

Chicago/Turabian Style

Hsuang-Chang Chiang; Faa-Jeng Lin; Jin-Kuan Chang. 2017. "Novel Control Method for Multimodule PV Microinverter With Multiple Functions." IEEE Transactions on Power Electronics 33, no. 7: 5869-5879.