RETRACTED ARTICLE: Deep perceptron neural network with fuzzy PID controller for speed control and stability analysis of BLDC motor

Gobinath, S.; Madheswaran, Muthusamy

doi:10.1007/s00500-019-04532-z

Cited by 57 publications

(35 citation statements)

References 28 publications

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“…[13] In Ref. [14], a three‐phase BLDC motor with a PID‐based speed controller for the four‐quadrant operation was presented. The PID is the appropriate speed control for the BLDC motor from the literature 15 ; however, the PID controller generates a poor steady‐state error.…”

Section: Methodsmentioning

confidence: 99%

Design methodology and experimental verification of intelligent speed controllers for sensorless permanent magnet Brushless DC motor

Vanchinathan¹,

Valluvan²,

Chinnaraj

et al. 2021

Int Trans Electr Energ Syst

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This article presents a modified genetic algorithm (MGA) to determine the five degrees of freedom parameters, namely K p , K i , K d , λ, and μ of a fractional order proportional integral derivative (FOPID) controller to achieve the speed control of a brushless direct current (BLDC) motor by sensorless technique. The MGA is a meta-heuristic inspired algorithm for solving nonlinearity problems such as sudden load disturbances, power fluctuations, and misalignment of the motor. The conventional genetic algorithm (CGA) is not very effective in solving the abovementioned problems. Hence, MGA-optimized FOPID (MGA-FOPID) controller is recommended for sensorless speed control of BLDC motor under varying load (T L ) conditions, and varying set speed (N s ) conditions. The proposed design of the MGA-FOPID controller has been implemented in MATLAB 2019a with Simulink models for optimal speed control of the BLDC motor. Also, the hardware experimental set-up and the results of the proposed controller are presented. The performance of the MGA-FOPID controller is observed and evaluated for time-domain characteristics. It is important to note that the proposed MGA-FOPID controller is more effective than the MGA optimized integer order proportional integral derivative controller in terms of minimizing time integral performance indexes.

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Section: Methodsmentioning

confidence: 99%

Design methodology and experimental verification of intelligent speed controllers for sensorless permanent magnet Brushless DC motor

Vanchinathan¹,

Valluvan²,

Chinnaraj

et al. 2021

Int Trans Electr Energ Syst

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show abstract

“…In addition, there are uncertainties due to load changes. In [29], the authors use a new deep perceptron neural network fuzzy adjusted PID controller to control the speed of BLDCM. The experimental results show that the proposed controller does have good robustness and stability to ensure the accurate operation of the motor.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, there build a simulation model of the brushless DC motor control system in Matlab/Simulink environment. The performance of AFPID is compared with the traditional PID control method [11], Fuzzy Logic PID control method [22], Genetic algorithm optimized fuzzy PID control method [26], and deep perceptron neural network fuzzy PID control method [29]. The results show that AFPID has a better control effect under the influence of different load disturbances and variable speeds.…”

Section: Introductionmentioning

confidence: 99%

An adaptive fuzzy PID controller for speed control of brushless direct current motor

Wang

et al. 2022

SN Appl. Sci.

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Aiming at the problems of the poor adaptive ability in the current control methods for brushless DC motor, an adaptive fuzzy proportional integral derivative controller (AFPID) is proposed to realize the better control performance of speed for brushless DC motor. AFPID includes a conventional PID controller (C-PID) and PI + PD architectures with a configurable fuzzy logic controller (C-PID-FLC). The FLC in C-PID-FLC consists of two fuzzy inference engines, one is used to self-tune the parameters for PI control, the other is for the scaling factor self-tuning of PI control parameters. The PD structure in C-PID-FLC is mainly to reduce oscillation, overcome overshoot and speed up system response while effectively eliminating static errors. When the system reaches a certain stable state of rotation, AFPID adjusts the C-PID controller ground on the speed error, which saves control costs under the premise of ensuring control performance. AFPID adaptively realizes the respective advantages of C-PID and C-PID-FLC. Compared with other control methods, the merits of the proposed controller are highlighted. The results show that the AFPID controller has a better control performance regardless of changes in load disturbance and parameter variations. And through the change of mechanical parameters of brushless DC motor, the sensitivity of AFPID is analyzed.

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“…Finally, the PID control has also been combined with more complex intelligent algorithms as deep neural networks. In [ 31 ] such a controller was designed for the speed control of a BLDC motor and its stability analysis was presented. In summary, the best performance for a multirotor control scheme is usually obtained by combining control techniques that add up features as disturbance robustness, energy efficiency, transient characteristics, and steady state error.…”

Section: Introductionmentioning

confidence: 99%

Velocity Sensor for Real-Time Backstepping Control of a Multirotor Considering Actuator Dynamics

Mayorga-Macías

González-Jiménez

Meza-Aguilar

et al. 2020

Sensors

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A real-time implementation of a control scheme for a multirotor, based on angular velocity sensors for the actuators, is presented. The control scheme is composed of two loops: an inner loop for the actuators and an outer loop for the unmanned aerial vehicle (UAV). The UAV control algorithm is designed by means of the backstepping technique and a robust sliding mode differentiator, and the actuator control strategy is based on a standard proportional-integral-derivative (PID) controller. A robust exact differentiator, based on high order sliding modes, is used to estimate the complex derivatives present in the proposed control law. As the measurements of the propeller’s angular velocities are required for the control law, velocity sensors are mounted in the axles of the rotors to retrieve them and a signal conditioning stage is implemented. In addition, dynamical models for the actuators of the aircraft were calculated by means of transfer functions obtained via experimental measurements in a test bench developed for this purpose. This test bench permits to characterize the parameters of the transfer functions by comparing the forces computed using the nominal parameter to the measured forces. To this end, it is assumed that the loads in the actuators of the vehicle are insignificant during flight. The effectiveness of the proposed sensor, its signal conditioning, and the overall control scheme are validated by means of simulation results and real-time experiments.

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RETRACTED ARTICLE: Deep perceptron neural network with fuzzy PID controller for speed control and stability analysis of BLDC motor

Cited by 57 publications

References 28 publications

Design methodology and experimental verification of intelligent speed controllers for sensorless permanent magnet Brushless DC motor

Design methodology and experimental verification of intelligent speed controllers for sensorless permanent magnet Brushless DC motor

An adaptive fuzzy PID controller for speed control of brushless direct current motor

Velocity Sensor for Real-Time Backstepping Control of a Multirotor Considering Actuator Dynamics

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