An Improved FPGA Implementation of Direct Torque Control for Induction Machines

Sutikno, Tole; Idris, Nik Rumzi Nik; Jidin, Auzani; Cirstea, Marcian

doi:10.1109/tii.2012.2222420

Cited by 79 publications

(55 citation statements)

References 38 publications

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“…The losing control problem of the BDFM's conventional DTC (the selected voltage vectors cannot meet the control demands of flux and torque simultaneously in some time intervals, the torque ripple goes outside the hysteresis band) is analyzed specifically by Equations (18) and (20) in the following.…”

Section: Losing Control Problem Of Conventional Direct Torque Controlmentioning

confidence: 99%

“…The derivative curves of the CM stator flux amplitude and the torque are obtained by Equations (18) and (20), as shown in Figure 3a,b. Where the CM stator flux amplitude is 0.8 Wb, the motor speed is 300 r/min (subsynchronous), and the output torques are rated torques, 350 Nm (motoring mode) and −350 Nm (generating mode).…”

Section: Losing Control Problem Of Conventional Direct Torque Controlmentioning

confidence: 99%

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Flux-Angle-Difference Feedback Control for the Brushless Doubly Fed Machine

2018

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Abstract:In direct torque control (DTC) of the brushless doubly fed machine (BDFM) system, the inverter switching voltage vectors cannot always meet the control requirements, and the torque will lose control. For the losing control problem, this paper presents a solution of indirectly controlling torque by controlling the angle difference between the power motor (PM) stator flux and the control motor (CM) stator flux (called as the flux-angle-difference). Firstly, based on the CM static coordinate system BDFM model, the derivative equations of CM stator flux amplitude, the torque, and the flux-angle-difference are deduced. The losing control problem of BDFM's DTC is studied by utilizing the CM stator flux amplitude and the torque derivatives. From the flux-angle-difference derivative, it is found that the phase angles of the flux-angle-difference derivative curves remain unchanged. Based on this property, by replacing the torque hysteresis comparator of conventional DTC with a flux-angle-difference hysteresis comparator, a modified control strategy called flux-angle-difference feedback control (FADFC) is proposed to solve the losing control problem. Finally, the validity and the good dynamic characteristic of the FADFC strategy are verified by simulation results.

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Section: Losing Control Problem Of Conventional Direct Torque Controlmentioning

confidence: 99%

Section: Losing Control Problem Of Conventional Direct Torque Controlmentioning

confidence: 99%

Flux-Angle-Difference Feedback Control for the Brushless Doubly Fed Machine

2018

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“…A comparison between Figures 10 and 11 clearly reveals that the proposed architecture operated with smaller ripples. The performance of the proposed PDTC was superior to that of the conventional DTC [22]. Figure 12 presents the locus of the stator flux in the proposed fuzzy PDTC, which was superior to that in [23].…”

Section: Simulation and Measurement Resultsmentioning

confidence: 94%

Predictive Direct Torque Control Application-Specific Integrated Circuit of an Induction Motor Drive with a Fuzzy Controller

Sung

Wang

Lin

et al. 2017

JLPEA

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This paper proposes a modified predictive direct torque control (PDTC) application-specific integrated circuit (ASIC) of a motor drive with a fuzzy controller for eliminating sampling and calculating delay times in hysteresis controllers. These delay times degrade the control quality and increase both torque and flux ripples in a motor drive. The proposed fuzzy PDTC ASIC calculates the stator's magnetic flux and torque by detecting the three-phase current, three-phase voltage, and rotor speed, and eliminates the ripples in the torque and flux by using a fuzzy controller and predictive scheme. The Verilog hardware description language was used to implement the hardware architecture, and the ASIC was fabricated by the Taiwan Semiconductor Manufacturing Company through a 0.18-µm 1P6M CMOS process that involved a cell-based design method. The measurements revealed that the proposed fuzzy PDTC ASIC of the three-phase induction motor yielded a test coverage of 96.03%, fault coverage of 95.06%, chip area of 1.81 × 1.81 mm 2 , and power consumption of 296 mW, at an operating frequency of 50 MHz and a supply voltage of 1.8 V.

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“…However, we can briefly mention some successful applications of FPGA devices, for instance, as power conversion controllers (as pulse width-modulation (PWM) inverters [24,25] and multilevel converters [26,27]). Uses can also be found in the control of electrical machines and in robotics applications (induction machine drives [28,29] and motion control [30,31]). More recently, reconfigurable devices have been increasingly finding applications as means for implementing hardware-in-the-loop platforms [32,33] for debugging purposes principally but increasingly as a means to emulate subcomponents of larger systems.…”

Section: Trends In the Use Of Fpgas For Implementing Iedsmentioning

confidence: 99%

Real-Time Control System for Improved Precision and Throughput in an Ultrafast Carbon Fiber Placement Robot Using a SoC FPGA Extended Processing Platform

Ochoa-Ruiz

Bevan

Lamotte

et al. 2017

International Journal of Reconfigurable Computing

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We present an architecture for accelerating the processing and execution of control commands in an ultrafast fiber placement robot. The system consists of a robotic arm designed by Coriolis Composites whose purpose is to move along a surface, on which composite fibers are deposed, via an independently controlled head. In first system implementation, the control commands were sent via Profibus by a PLC, limiting the reaction time and thus the precision of the fiber placement and the maximum throughput. Therefore, a custom real-time solution was imperative in order to ameliorate the performance and to meet the stringent requirements of the target industry (avionics, aeronautical systems). The solution presented in this paper is based on the use of a SoC FPGA processing platform running a real-time operating system (FreeRTOS), which has enabled an improved comamnd retrieval mechanism. The system's placement precision was improved by a factor of 20 (from 1 mm to 0.05 mm), while the maximum achievable throughput was 1 m/s, compared to the average 30 cm/s provided by the original solution, enabling fabricating more complex and larger pieces in a significant fraction of the time.

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An Improved FPGA Implementation of Direct Torque Control for Induction Machines

Cited by 79 publications

References 38 publications

Flux-Angle-Difference Feedback Control for the Brushless Doubly Fed Machine

Flux-Angle-Difference Feedback Control for the Brushless Doubly Fed Machine

Predictive Direct Torque Control Application-Specific Integrated Circuit of an Induction Motor Drive with a Fuzzy Controller

Real-Time Control System for Improved Precision and Throughput in an Ultrafast Carbon Fiber Placement Robot Using a SoC FPGA Extended Processing Platform

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