scholarly journals PERFORMANCE IMPROVEMENT ON MOTOR BLDC SPEED CONTROLLER BY USING MULTI CONTROLLER WITH SUMMATION TECHNIQUE

2021 ◽  
pp. 1-8
Author(s):  
Widjonarko ◽  
Cries Avian ◽  
Setya Widyawan Prakosa ◽  
Bayu Rudiyanto
Keyword(s):  
Control System ◽  
Fuzzy Logic Controller ◽  
Absolute Error ◽  
Bldc Motor ◽  
Motor Speed ◽  
Electric Cars ◽  
Speed Controller ◽  
The Difference ◽  
Increasing Demand ◽  
Artificial Neural Network Ann

BLDC motor is the most widely used in the industrial world, especially in electric vehicles. With this increasing demand, a variety of research topics emerged in BLDC motors. One popular research is on BLDC motor speed control topics to maintain speed for its application, such as intelligent cruise technology in electric cars and conveyors for line assembly. However, from several existing studies, the BLDC Motor controller still uses a single controller model. The controller's output is purely from the controller without any improvement in characteristics and has a problem with the oscillating speed setpoint (error problem). In this study, the researcher proposed a combining control with the concept of summation output to handle this problem. With this concept, the control techniques used can improve each other so that better control can be produced following the control system assessment parameters. The authors used a Fuzzy Logic Controller, Artificial Neural Network (ANN), and PID, which were combined and obtained seven control systems. The results show that the control system can improve several parameters using the summation concept from the seven controllers model. It has a positive overall correlation when viewed in terms of the difference between the Error and the setpoint or MAE (Mean Absolute Error) as parameter assessment.

scholarly journals Comparison between Fuzzy Logic and PI Control for The Speed Of BLDC Motor

2018 ◽  
Vol 9 (3) ◽  
pp. 1116 ◽  
Author(s):  
Akram H. Ahmed ◽  
Abd El Samie B. Kotb ◽  
Ayman M. Ali
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Dynamic Performance ◽  
Bldc Motor ◽  
Motor Speed ◽  
Proportional Integral ◽  
Brushless Dc ◽  
Speed Controller ◽  
Torque Response ◽  
High Degree

In this paper the analytical comparison of brushless DC (BLDC) motor drive with proportional integral (PI) and fuzzy logic controller (FLC) based speed controllers is estimated. Proportional integral (PI) has disadvantages like it do not operate properly when the system has a high degree of load disturbances.<em> </em>In recent years, the application of fuzzy logic controller (FLC) for high dynamic performance of motor drives has become an important tool. FLC is a good for load disturbances and can be easily implemented. The modeling and simulation of both the speed controllers have been made by MATLAB/SIMULINK. The dynamic characteristics of the BLDC motor (speed and torque) response, obtained under PI and Fuzzy logic based speed controller, are compared for various operating condition.

scholarly journals BLDC Motor Control Optimization Using Optimal Adaptive PI Algorithm

2020 ◽  
Vol 20 (2) ◽  
pp. 47
Author(s):  
Supriyanto Praptodiyono ◽  
Hari Maghfiroh ◽  
Chico Hermanu
Keyword(s):  
Fuzzy Logic Controller ◽  
Absolute Error ◽  
Pi Controller ◽  
Settling Time ◽  
The Other ◽  
Step Response ◽  
Pi Control ◽  
Bldc Motor ◽  
Motor Speed ◽  
Control Effort

The main problem of using a Proportional Integral (PI) Controller in Brushless Direct Current (BLDC) motor speed control is tuning the PI’s parameter and its performance cannot adapt to the system behavior changes. Particle Swarm Optimization (PSO) has been chosen to optimize the tuning. Fuzzy Logic Controller (FLC) is used to online tuning PI’s parameters to adapt to system conditions. Optimal adaptive PI, which combines the PSO method and FLC method to tune PI, is proposed. It was successfully implemented in the simulation environment. The test was carried out in three conditions: step responses, set-point changes, and disturbance rejection. The proposed algorithm is superior with no overshoot/undershoot. Whereas in terms of settling time is in between PI and PI-PSO. PI controller has the smallest control effort. However, the other parameter is the worst. PI-PSO is superior in terms of settling time and Integral of Absolute Error (IAE) except for the step response test. The proposed method has lower IAE and higher control effort by 78.73 % and 60 % compared to PI control. On the other hand, it has a higher IAE dan lower control effort by 11.82 % and 33.88 % compared to PI-PSO. Therefore, the optimal adaptive PI control can reduce energy consumption compared to optimal PI with better performance than PI control.

Artificial Neural Networks Used As a Rule Selector for Fuzzy Logic Controllers

1993 ◽  
Author(s):  
Shou-Heng Huang ◽  
Ron M. Nelson
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Logic Controller ◽  
Absolute Error ◽  
Operating Conditions ◽  
Delta Rule ◽  
Rule Sets ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Set Up ◽  
And Control

Abstract A feedforward, three-layer, partially-connected artificial neural network (ANN) is proposed to be used as a rule selector for a rule-based fuzzy logic controller. This will allow the controller to adapt to various control modes and operating conditions for different plants. A principal advantage of an ANN over a look up table is that the ANN can make good estimates to fill in for missing data. The control modes, operating conditions, and control rule sets are encoded into binary numbers as the inputs and outputs for the ANN. The General Delta Rule is used in the backpropagation learning process to update the ANN weights. The proposed ANN has a simple topological structure and results in a simple analysis and relatively easy implementation. The average square error and the maximal absolute error are used to judge if the correct connections between neurons are set up. Computer simulations are used to demonstrate the effectiveness of this ANN as a rule selector.

scholarly journals DC Motor Speed Control Using Mamdani Fuzzy Logic Based on Microcontroller

Jurnal Teknik ◽  
2020 ◽  
Vol 9 (2) ◽  
Author(s):  
Sumardi Sadi
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Fuzzy Logic Control ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Dc Motor ◽  
Motor Speed ◽  
Logic Control ◽  
Control Command ◽  
Arduino Uno

DC motors are included in the category of motor types that are most widely used both in industrial environments, household appliances to children's toys. The development of control technology has also made many advances from conventional control to automatic control to intelligent control. Fuzzy logic is used as a control system, because this control process is relatively easy and flexible to design without involving complex mathematical models of the system to be controlled. The purpose of this research is to study and apply the fuzzy mamdani logic method to the Arduino uno microcontroller, to control the speed of a DC motor and to control the speed of the fan. The research method used is an experimental method. Global testing is divided into three, namely sensor testing, Pulse Width Modulation (PWM) testing and Mamdani fuzzy logic control testing. The fuzzy controller output is a control command given to the DC motor. In this DC motor control system using the Mamdani method and the control system is designed using two inputs in the form of Error and Delta Error. The two inputs will be processed by the fuzzy logic controller (FLC) to get the output value in the form of a PWM signal to control the DC motor. The results of this study indicate that the fuzzy logic control system with the Arduino uno microcontroller can control the rotational speed of the DC motor as desired.

scholarly journals Brushless DC Motor Speed Controller for Electric Motorbike

2018 ◽  
Vol 9 (2) ◽  
pp. 859
Author(s):  
Mohd Syakir Adli ◽  
Noor Hazrin Hany Mohamad Hanif ◽  
Siti Fauziah Toha Tohara
Keyword(s):  
Pid Controller ◽  
Dc Motor ◽  
Battery Pack ◽  
Brushless Dc Motor ◽  
Bldc Motor ◽  
Motor Speed ◽  
Brushless Dc ◽  
Speed Controller ◽  
Optimum Speed ◽  
Control Scheme

<p>This paper presents a control scheme for speed control system in brushless dc (BLDC) motor to be utilized for electric motorbike. While conventional motorbikes require engine and fuel, electric motorbikes require DC motor and battery pack in order to be powered up. The limitation with battery pack is that it will need to be recharged after a certain period and distance. As the recharging process is time consuming, a PID controller is designed to maintain the speed of the motor at its optimum state, thus ensuring a longer lasting battery time (until the next charge). The controller is designed to track variations of speed references and stabilizes the output speed accordingly. The simulation results conducted in MATLAB/SIMULINK® shows that the motor, equipped with the PID controller was able to track the reference speed in 7.8x10<sup>-2</sup> milliseconds with no overshoot.  The result shows optimistic possibility that the proposed controller can be used to maintain the speed of the motor at its optimum speed.</p>

Characteristic of Fuzzy, ANN, and ANFIS for Brushless DC Motor Controller: An Evaluation by Dynamic Test

2021 ◽  
Vol 13 (6) ◽  
Author(s):  
◽  
Andi Setiawan ◽  
Bayu Rudiyanto ◽  
Satryo Budi Utomo ◽  
Muji Muji Setiyo ◽  
...  
Keyword(s):  
Fuzzy Logic Controller ◽  
Fuzzy Inference ◽  
Dynamic Testing ◽  
Dynamic Test ◽  
Absolute Error ◽  
Inference System ◽  
Brushless Dc ◽  
Average Value ◽  
Constant Torque ◽  
Artificial Neural Network Ann

Brushless DC (BLDC) motors are the most popular motors used by the industry because they are easy to control. BLDC motors are generally controlled by artificial controls such as Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS). However, the performance of the BLDC control system in previous studies was compared separately with their respective parameters, making it difficult to evaluate comprehensively. Therefore, in order to investigate the characteristic performance of Fuzzy, ANN, and ANFIS, this article provides a comparison of these artificial controls. Two scenarios of the dynamic tests are conducted to investigate control performance under constant torque-various speed and constant speed-various torque. By dynamic testing, characteristics of Fuzzy, ANN, and ANFIS can be observed as real applications. The testing parameters are: Settling Time, Overshoot and Overdamp (in the graph and average value), and then statistic performance are: Integral Square Error (ISE), Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE), and Mean Absolute Error (MAE). The test result in scenario 1 showed that the ANN has a better performance compared to other controllers with the MAE, IAE, ITAE, and ISE value of 31.3003; 105.6280; 208.0630; and 5,7289 e4, respectively. However, in scenario 2, ANN only has a better performance compared to other controllers on just a few parameters. In scenario 2, ANN is indeed able to maintain speed but it has a more ripple value than ANFIS. Even so, the ripple that occurs in ANN does not have too much value compared to the setpoint. Therefore, the MAE value of the ANN is smaller than the ANFIS (18.8937 of ANN and 28.4685 of ANFIS).

A reconfigurable structure electronic commutator for a dual BLDC motor EV drive

2012 ◽  
Vol 60 (4) ◽  
pp. 769-778
Author(s):  
T. Biskup ◽  
A. Bodora ◽  
A. Domoracki
Keyword(s):  
Control System ◽  
Electric Vehicle ◽  
Motor Drives ◽  
Bldc Motor ◽  
Motor Speed ◽  
Common Control ◽  
Speed Range ◽  
Constant Power Speed Range ◽  
Reconfigurable Structure ◽  
Electronic Commutator

Abstract An electronic commutator that can drive a PM BLDC motor either in the full bridge or half bridge configuration has been developed. This commutator allows increasing the motor speed over the nominal value, hence the motor is able to operate within a wide constant power speed range. An application of the commutator with a reconfigurable structure for the double drive of a small electric vehicle Elipsa has been presented. The driveline consists of two independent commutators feeding the motors coupled by gears with rear wheels of the vehicle. Both commutators are controlled by a common control system based on a signal microcontroller. The results of road tests indicate new areas of BLCD motor drives application. The fact that the BLCD motor work in the second speed range does not require any changes in the motor construction and at the same time does not significantly deteriorate the drive efficiency is an indisputable advantage of the presented solution

Application of Photoelectric Sensor in Vehicle Power Control System

2020 ◽  
Vol 15 (6) ◽  
pp. 700-706
Author(s):  
Yifan Zhao ◽  
Mengyu Wang ◽  
Kai Wang
Keyword(s):  
Power Control ◽  
Electric Energy ◽  
Least Square Method ◽  
Least Square ◽  
Motor Speed ◽  
Time Calculation ◽  
The Mean ◽  
The Difference ◽  
Artificial Neural Network Ann ◽  
Mean Square Errors

Due to its characteristics of using clean electric energy and bringing no damage to the environment, electric vehicles (EVs) have become a new developmental direction for the automotive industry. Its reliability issues have also attracted the attention of experts and professionals. In the field of automotive power control, from the perspective of motor control, this study uses the photoelectric sensors (PSs) as the research objects and elaborates on the measurement principles of motor speed with PSs. Meanwhile, a diagnosis scheme is proposed for various faults in the measurement. Among them, the measurement speed is converted by the photoelectric signal, and the measured waveform is amplified. In the fault detection process, the Radial Basis Function (RBF) artificial neural network (ANN) is analyzed. By using this method, the difference in the motor speed detected by the sensor is calculated to determine the cause of the failure. The test uses the least-square method to compare the tested motor speed with the actual motor speed. The results show that PSs can measure the motor speed of EVs. As for the motor failures, the mean square errors (MSEs) of motor speeds generated by different faults are compared to determine the fault points according to the speed changes. In addition, the cause of motor failure can be determined by the real-time calculation of the speed differences. The above tests fully prove the effectiveness of measuring the speed of electric motors by PSs; therefore, PSs have broad application prospects in vehicle power control systems.

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