Design and Implementation of Particle Swarm Optimization (PSO) Tuned PID Controller for Speed Control of Permanent Magnet Brush Less DC (PMBLDC) Motor

Ramya, M.; Jadhav, Sharad P.; Pawar, Sushant N.

doi:10.1109/incet49848.2020.9154039

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…The results show that adaptive controllers, such as neuron networks, genetic algorithms, Fuzzy logic regulator and others, allow improve the dynamics of the motor even they do not require a precise motor model to create such a controller. Sliding mode control for BLDC motor control has been used in the works referenced in [7,8], model-based adaptive control in the scientific works presented in [9,10], genetic algorithm based control in the works presented in [11,12], neural network control algorithm in the works presented in [13,14], swarm optimization algorithm in the works presented in [15,16], robust control was proposed in the works presented in [17,18]. Although these methods showed good performance at least in simulation, for implementation there is a need for a high-performance controller and the model of the motor should be known in some cases.…”

Section: Introductionmentioning

confidence: 99%

BLDC Motor Speed Control with Digital Adaptive PID-Fuzzy Controller and Reduced Harmonic Content

Kroičs,

Būmanis

2024

Energies

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Low power brushless direct current (BLDC) motors are used in many consumer appliances. These motors have a relatively high winding resistance and therefore current control loop can be avoided in some cases, but fast and accurate speed regulation can be still needed. To minimize harmonics and generated sound noise, improved sinusoidal pulse width modulation (PWM) has been tested in the paper. As the most suitable commutation type, the sine wave with the third harmonic component has been selected. This type of communication reduced the torque ripple of the motor. This paper analyses the possibility to improve traditional proportional-integral-derivative (PID) speed regulator with Fuzzy logic block. A simulation model of BLDC motor, inverter, speed detection circuit and controller have been created. Simulation results showed that by applying the Fuzzy-based PID controller, the transient time can be reduced from 0.2 s to 0.05 s and overshoot can be avoided in comparison with traditional PID controller. Experimental results show a significant improvement in the motor dynamics—the overshoot and transient time were reduced twice. The difference with simulation results and experimental ones can be explained by delays introduced by the microcontroller.

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Section: Introductionmentioning

confidence: 99%

BLDC Motor Speed Control with Digital Adaptive PID-Fuzzy Controller and Reduced Harmonic Content

Kroičs,

Būmanis

2024

Energies

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“…Open-loop control shows the actual operation of the system according to the input signal, but a closed-loop system is used for the desired output with diferent input signals [11]. A PID controller is utilized as a control system to control appliances by tuning the parameters [12,13].…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Enabled Intelligent Automation for Smart Home with the Integration of PSO Algorithm and PID Controller

Singh

Gehlot

Kuchhal

et al. 2023

Journal of Electrical and Computer Engineering

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Currently, due to the widespread population growth, there is a widespread concern about an electricity shortage. As a result, smart devices have evolved and gained significant attention to reduce power consumption in home appliances due to electricity shortages. However, it lacks a universal remote control that can control home appliances based on environmental conditions. To overcome these challenges, this study proposed a hardware-based remote-control system that operates both in autonomous and semiautonomous modes to control home appliances based on environmental conditions. In the autonomous mode, the receiver section regulates the parameters under ambient conditions by varying the appliance’s applied voltage levels via a dimmer. The parameters in semiautonomous are monitored by the user via various levels of remote control. A 2.4 GHz RF modem is used to establish wireless personal network (WPAN) communication between the remote and the receiver. In addition, a Wi-Fi modem is built into the receiver to enable internet-based mobile applications to operate appliances. During the MATLAB analysis, a proportional integral derivative (PID) controller with a particle swarm optimization (PSO) method was found as a superior approach to control the home appliance with adequate environmental conditions. It is concluded from the MATLAB study that the PSO-PID controller delivered an energy saving of 14.88% for the heater, 36.9% for the exhaust fan, and 37.49% for the light bulb compared to the conventional appliances.

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“…In the last few years, a large number of contributions based on various advanced intelligent control strategies such as sliding-mode control [9,10], ant colony algorithm [11,12], genetic algorithm [13,14], particle swarm optimization (PSO) algorithm [15,16], model reference adaptive control (MRAC) algorithm [17,18], fuzzy control [19,20], artificial neural network control algorithm [21,22], and so on, have been proposed to solve this problem. Among them, the fuzzy control algorithm is widely used in various control systems because it has the advantage of not needing to master the mathematical model of the controlled object but organizes the control decision table according to manual rules and then determines the size of the control amount.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Neural Network PID Algorithm for BLDCM Speed Control System

Yin

Wang

et al. 2021

Mathematics

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Because of its simple structure, high efficiency, low noise, and high reliability, the brushless direct current motor (BLDCM) has an irreplaceable role compared with other types of motors in many aspects. The traditional proportional integral derivative (PID) control algorithm has been widely used in practical engineering because of its simple structure and convenient adjustment, but it has many shortcomings in control accuracy and other aspects. Therefore, in this paper, a fuzzy single neuron neural network (FSNNN) PID algorithm based on an automatic speed regulator (ASR) is designed and applied to a BLDCM control system. This paper introduces a BLDCM mathematical model and its control system and designs an FSNNN PID algorithm that takes speed deviation e at different sampling times as inputs of a neural network to adjust the PID parameters, and then it uses a fuzzy system to adjust gain K of the neural network. In addition, the frequency domain stability of a double closed loop PID control system is analyzed, and the control effect of traditional PID, fuzzy PID, and FSNNN PID algorithms are compared by setting different reference speeds, as well as the change rules of three-phase current, back electromotive force (EMF), electromagnetic torque, and rotor angle position. Finally, results show that a motor controlled by the FSNNN PID algorithm has certain superiority compared with traditional PID and fuzzy PID algorithms and also has better control effects.

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Design and Implementation of Particle Swarm Optimization (PSO) Tuned PID Controller for Speed Control of Permanent Magnet Brush Less DC (PMBLDC) Motor

Cited by 10 publications

References 8 publications

BLDC Motor Speed Control with Digital Adaptive PID-Fuzzy Controller and Reduced Harmonic Content

BLDC Motor Speed Control with Digital Adaptive PID-Fuzzy Controller and Reduced Harmonic Content

Internet of Things Enabled Intelligent Automation for Smart Home with the Integration of PSO Algorithm and PID Controller

Adaptive Fuzzy Neural Network PID Algorithm for BLDCM Speed Control System

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