Speed control and quantum vibration reduction of Brushless DC Motor using FPGA based Dynamic Power Containment Technique

Saravanan, S.; Rammohan, T.; Shanmugam, Sathish Kumar; Bačanin, Nebojša; Živković, Miodrag; Strumberger, Ivana

doi:10.1007/s12652-021-02969-5

Cited by 8 publications

(4 citation statements)

References 29 publications

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“…The active control system takes the role of the conventional control system, which controlled the speed and current of the BLDC motor. The suggested Field Programmable Gate Arrays (FPGAs) are controlled directly on the inverter input power with the aid of the Dynamic Power Containment Technology (DPCT) control system [2], which is based on Dynamic Power Containment Technology (DPCT). When this occurs, the magnetic flux density, flux distribution, and torque of finite element models will only be utilized to evaluate motor performance and to analyse and approve the design of the system [3,4].…”

Section: Review Of the Literaturementioning

confidence: 99%

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Energy Auditing in Three-Phase Brushless DC Motor Drive Output for Electrical Vehicle Communication Using Machine Learning Technique

Ramana

Manaktala

Valarmathi³

et al. 2022

Wireless Communications and Mobile Computing

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Using predictive nonlinear optimal control, this model examines the output power of a three-phase brushless DC motor (BLDC) drive to ensure that it is stabilized (PNOC). A BLDC is a kind of electric motor that is used in a variety of applications and is one of the models of electric motors that are utilized in constant speed applications. In this motor, the movable component of the rotor created torque and the rotor rotated in a position of low reluctance; the location of the rotor is determined by the motor’s maximum inductance value. The BLDC drive controls the motor via the converter circuit, and the converter circuit ensures that the motor receives the appropriate output power. The project manager should have a thorough discussion with the team about the demagnetization of the malfunctioning BLDC motor before beginning this job. It is possible to model a machine using many existing technologies, such as electrical equivalent circuit diagram (EEC), which are based on a number of assumptions that make the analysis process or the analysis approach simpler. Despite numerical methodologies, these approach scenarios give frequency domain loop (FDL) precision frequency domain, using a suitable weight strategy to deliver high power solution creation (NM). The purpose of this essay is to integrate these two technologies in order to make contributions via the development of a new hybrid EEC-FDL model closed-loop brushless DC motor. PNOC is a driving system that uses predictive nonlinear optimal control (PNOC). The generated model is subjected to simulations under both healthy and incorrect settings, respectively. MATLAB software is utilized to construct the simulation of the control circuit, and simulation outputs are validated by experimental findings. Predictive nonlinear optimal control (PNOC) is employed to eliminate torque ripple and improve system stability.

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Section: Review Of the Literaturementioning

confidence: 99%

“…In truth, motors utilize the majority of the energy, regardless of the situation at hand applicability in a residential, industrial, or commercial setting. Its energy efficiency is high [2]. The kind of motor determines the performance of the motor.…”

Section: Introductionmentioning

confidence: 99%

Energy Auditing in Three-Phase Brushless DC Motor Drive Output for Electrical Vehicle Communication Using Machine Learning Technique

Ramana

Manaktala

Valarmathi³

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…They have the advantage of better accuracy, repeatability, and lower noise sensitivity. Also, it is compatible with other types of preprocessors [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Implementation of ANN-Based Auto-Adjustable for a Pneumatic Servo System Embedded on FPGA

et al. 2022

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Artificial intelligence techniques for pneumatic robot manipulators have become of deep interest in industrial applications, such as non-high voltage environments, clean operations, and high power-to-weight ratio tasks. The principal advantages of this type of actuator are the implementation of clean energies, low cost, and easy maintenance. The disadvantages of working with pneumatic actuators are that they have non-linear characteristics. This paper proposes an intelligent controller embedded in a programmable logic device to minimize the non-linearities of the air behavior into a 3-degrees-of-freedom robot with pneumatic actuators. In this case, the device is suitable due to several electric valves, direct current motors signals, automatic controllers, and several neural networks. For every degree of freedom, three neurons adjust the gains for each controller. The learning process is constantly tuning the gain value to reach the minimum of the mean square error. Results plot a more appropriate behavior for a transitive time when the neurons work with the automatic controllers with a minimum mean error of ±1.2 mm.

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“…More and more intelligence is being added to systems every day. Intelligent measurement [1]- [3], control [4], [5] and assistance systems [6], [7] in various sectors. Not only are embedded systems multiplying and diversifying, but they are also becoming more and more complex.…”

Section: Introductionmentioning

confidence: 99%

Proposal of a reliable embedded circuit to control a stepper motor using microblaze soft-core processor

Zemmouri

Barodi

Satif

et al. 2022

IJRES

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The vast majority of embedded system designs start with a processor-based system, using a microcontroller or microprocessor as the core element to predict and process the basic control tasks. This paper aims to increase the reliability of controlling a stepper motor by an embedded circuit, which generates four digital signals of pulse width modulation (PWM). The manager of this circuit is an embedded processor licensed from Microblaze, which sits within the FPGA architecture Virtex 5. This processor analyzes the parameters to execute the rotation under the best conditions (energy consumption, speed, precision, reliability). The communication with the PC takes place via the RS232 link to provide feedback. The autonomous processor generates an independent control frequency of the PC. The four digital signals of PWM are simulated on Xilinx's ISim interface and sent to the ULN2803 amplifier circuit to run the stepper motor.

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Speed control and quantum vibration reduction of Brushless DC Motor using FPGA based Dynamic Power Containment Technique

Cited by 8 publications

References 29 publications

Energy Auditing in Three-Phase Brushless DC Motor Drive Output for Electrical Vehicle Communication Using Machine Learning Technique

Energy Auditing in Three-Phase Brushless DC Motor Drive Output for Electrical Vehicle Communication Using Machine Learning Technique

Implementation of ANN-Based Auto-Adjustable for a Pneumatic Servo System Embedded on FPGA

Proposal of a reliable embedded circuit to control a stepper motor using microblaze soft-core processor

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