SPEED CONTROL OF BLDC MOTOR USING NEURAL NETWORK MODELLING IN MATLAB/SIMULINK

ABSTRACT

The main purpose of this paper is to control the speed of a brushless DC motor using an Artificial Neural Network (ANN) controller and a PID controller. Detailed analysis was performed based on simulation results of both methods. A neural control based speed control system of brushless DC motor is designed by analyzing the mathematical model of the BLDC motor. Factory model recognition was performed on MATLAB's Simulink software to recognize the ANN block of the BLDC motor drive system. The reference control model is designed to give the ideal values of the control parameters when the control system responds to the command signal. The performance results of the PID controller and ANN controller are compared with the reference model output of the BLDC motor drive system in the MATLAB Simulink environment. The comparative study concluded that the ANN-based speed control method eliminates overshoot, reducing the response time of the system. It is observed that the simulation results based on ANN are closer to the response of the ideal reference control model than those based on PID.

АННОТАЦИЯ

Основной целью этой статьи является управление скоростью бесщеточного двигателя постоянного тока с использованием контроллера искусственной нейронной сети (ИНС) и ПИД-регулятора. Детальный анализ был выполнен на основе результатов моделирования обоих методов. Система управления скоростью бесщеточного двигателя постоянного тока на основе нейронного управления разработана путем анализа математической модели бесщеточного двигателя постоянного тока. Распознавание заводской модели было выполнено в программном обеспечении MATLAB Simulink для распознавания блока ANN системы электропривода BLDC. Эталонная модель управления предназначена для выдачи идеальных значений параметров управления, когда система управления реагирует на командный сигнал. Результаты производительности ПИД-регулятора и контроллера ANN сравниваются с выходными данными эталонной модели системы привода двигателя BLDC в среде MATLAB Simulink. В сравнительном исследовании сделан вывод о том, что метод управления скоростью на основе ИНС устраняет перерегулирование, сокращая время отклика системы. Замечено, что результаты моделирования на основе ИНС ближе к отклику идеальной эталонной модели управления, чем результаты на основе ПИД.

Keywords: Artificial Neural Networks (ANNs), Brushless Direct Current (BLDC) Motors, PI Controller, The motor modeled in the MATLAB/Simulink.

Ключевые слова: искусственные нейронные сети (ИНС), бесщеточные двигатели постоянного тока (BLDC), ПИ-контроллер, двигатель, смоделированный в MATLAB/Simulink.

1. Introduction

A brushless DC electric motor, also known as an electronically commutated motor, is a synchronous motor using a direct current (DC) electric power supply. It uses an electronic controller to switch DC currents to the motor windings producing magnetic fields which effectively rotate in space and which the permanent magnet rotor follows. The controller adjusts the phase and amplitude of the DC current pulses to control the speed and torque of the motor. This control system is an alternative to the mechanical commutator (brushes) used in many conventional electric motors. The construction of a brushless motor system is typically similar to a permanent magnet synchronous motor (PMSM), but can also be a switched reluctance motor, or an induction (asynchronous) motor. They may also use neodymium magnets and be outrunners (the stator is surrounded by the rotor), inrunners (the rotor is surrounded by the stator), or axial (the rotor and stator are flat and parallel) [1]. The advantages of a brushless motor over brushed motors are high power-to-weight ratio, high speed, nearly instantaneous control of speed (rpm) and torque, high efficiency, and low maintenance. Brushless motors find applications in such places as computer peripherals (disk drives, printers), hand-held power tools, and vehicles ranging from model aircraft to automobiles. In modern washing machines, brushless DC motors have allowed replacement of rubber belts and gearboxes by a direct-drive design [2].

Brushed DC motors were invented in the 19th century and are still common. Brushless DC motors were made possible by the development of solid state electronics in the 1960s [3]. An electric motor develops torque by keeping the magnetic fields of the rotor (the rotating part of the machine) and the stator (the fixed part of the machine) misaligned. One or both sets of magnets are electromagnets, made of a coil of wire wound around an iron core. DC running through the wire winding creates the magnetic field, providing the power which runs the motor. The misalignment generates a torque that tries to realign the fields. As the rotor moves, and the fields come into alignment, it is necessary to move either the rotor's or stator's field to maintain the misalignment and continue to generate torque and movement. The device that moves the fields based on the position of the rotor is called a commutator [4][5][6].

Brushless motors are widely used in electric vehicles, hybrid vehicles, personal transportation and electric aircraft [7]. Most e-bikes use a brushless motor that is sometimes integrated into the wheel hub, with the stator securely fixed to the shaft and the magnet attached to and rotating with the wheel. The same principle applies in self-balancing wheels. Most electrically powered radio controlled models use brushless motors because of their high efficiency.     

The BLDC motor is basically a synchronous motor. Which uses a constant current source. In BLDC motors, mechanical the commutator is replaced by an electronic servo system, making it reliable for determining the angle of the rotor and for controlling switches. And electronic feedback controllers are used to switch Supplying direct current to the motor windings to create magnetic fields.

The controller changes the amplitude and phase of DC pulses control the torque and speed of motors. Bridge Converter is a DC-DC converter topology that uses four active switching components in a bridge configuration via power transformer. Full bridge converter is popular configuration that provides isolation, as well as step-up or step-down input voltage. Reversing polarity and providing multiple output voltages are additional functions at the same time equipped with bridge converters. Speed controller in BLDC motor plays an important role in modern control systems. open cycle and feedback methods are the two main types of control system. Dual feedback control is a popular term when torque or current loop form an internal control loop, and the voltage or speed loop forms the outer control loop. When motor running below rated speed, input voltage the motor is modified using a Pulse Width Modulation strategy. When the motor goes beyond the rated speed, the flow weakens as the excitation current or auxiliary current advances. Several methodologies for speed control have been proposed. Control of BLDC motors. PID control is generally preferred as it is one of the most popular methods over the years and still is used in several applications. In addition, PID controllers are widely used because of its reliable property and more reliable. As a rule, PID controllers satisfy the needs speed regulation. Since the BLDC motor is a non-linear system with multiple variables, many issues need to be considered for solution. At present, for speed control, almost all BLDC motors employ PID controller for PWM.

Nowadays, the field of electrical power system control in general, and motor control in particular have been researching broadly. The new technologies are applied to these in order to design the complicated technology system. One of these new technologies is Artificial Neural Networks (ANNs) which base on the operating principle of human being nerve neural. There are a number of articles that use ANNs applications to identify the mathematical DC motor model. And then this model is applied to control the motor speed. They also uses inverting forward ANN with two input parameters for adaptive control of DC motor [8].

2. Electronic controllers

The proportional integral (PID) controller is a more widely used control system than the proportional integral derivative (PID) controller. The main need of the controller is not only to control the speed, but also to reduce the difference between the actual speed and the set speed. PI controller parameters, mainly gain, i.e. proportional and integral gain, affect the performance of the whole controller. Thus, setting parameters is a very important and difficult job. PI controllers are mainly used for processes that deal with the same input and perturbation and lead to the same output, i.e. no integration. The time-weighted absolute error integral and internal mode control methods are mainly used to tune the PI controller. The measured current, voltage and speed are transmitted to the controller.

                                     (1)

Figure 1. Schematic of PID controller

The working of the PI controller is based on the above equation. The error signal is calculated in the controller by taking difference of the actual and the reference values. In PI control the error is multiplied by proportionality and integral constant. The value we obtain will be in exponential order so in order to make it comparable it with other quantities it is passed on to a PWM signal converter. Then the PI controller controls the motor speed by changing the DC voltage fed in to motor winding through bridge converter as discussed above. A bridge converter is DC-DC converter which has better efficiency in contrast to a bridge rectifier. They enable to step-up and stepdown inputs like a transformer and also offer isolation.

Figure 2. BLDC closed loop control diagram

Figure 3. Basic flow chart of BL DC motor speed control

Figure 4. The BLDC Commutation Logic block implements a commutation logic for brushless DC motors as part of this control algorithm

Table 1.

The commutation logic is based on the Hall signals as summarized in this table

3. Neural network controller

The neural model reference control architecture uses two neural networks: a controller network and a plant model network, as shown in the following figure. The plant model is identified first, and then the controller is trained so that the plant output follows the reference model output.

Figure 5. The neural model reference control architecture uses two neural networks

The following figure shows the details of the neural network plant model and the neural network controller as they are implemented in the Neural Network Toolbox™ software. Each network has two layers, and you can select the number of neurons to use in the hidden layers. There are three sets of controller inputs:

• Delayed reference inputs
• Delayed controller outputs
• Delayed plant outputs

For each of these inputs, you can select the number of delayed values to use. Typically, the number of delays increases with the order of the plant. There are two sets of inputs to the neural network plant model:

• Delayed controller outputs
• Delayed plant outputs

As with the controller, you can set the number of delays. The next section shows how you can set the parameters.

Figure 6. The neural network plant model and the neural network controller as they are implemented in the Neural Network  software

4. Simulation results of BLDC motor

Figure 7. Mat Lab/Simulink model for BLDC motor Controller using PI and Neural Network

Figure 8. Performance curve for result validation

Figure 9. Stator current and back EMF of Phase A using cotroller PI

Figure 10. Stator current and back EMF of Phase A using cotroller Neural Network

Figure 11. Speed of BLDC motor with neural controller

Figure 12. Electromagnetic torque of BLDC motor

5. Conclusion

We developed a simulink model of Brush Less DC motor speed control by using the PID controller circuit. The gain parameters of PID module are control by Neural Network and self tuning. The speed control of Brush Less DC motor is control by Neural Network method more efficiently and provided stability to the different applications.

The result of the study is a dynamic implementation of the proposed controller based on a neural network. Research presented BLDC neural network motor controller with closed control loop. The efficiency and sensitivity of the controller have been verified by the program MATLAB-Simulink. Simulation results show that torque ripple and current ripple have been reduced, which improve transmission performance. It is concluded that applying load torque to the motor using a neural network controller,engine speed will not decrease. Speed control is realized by ANN soft control technology. The results achieved in this work justifies the choice of motor control methods, since with step-by-step loads the motor speed does not decrease when used. The proposed neural controller. The results of the proposed controller are confirmed by the reference results obtained by mathematical methods. For the system. For the learning process of ANN, multi-layer forward back propagation with gradient descent method is used.

The main philosophy of this article is to explore parameters such as rise time, steady time, and overshoot. with step load engine speed does not decrease. In conclusion, we note that the simulation results show torque and current ripples ripple is reduced, increasing the performance of the drive. With the neural controller, the BLDC motor has no error at steady state.

The neural network controller feedback is highly efficient. Results for positive results of dynamic execution BLDC motors in different load situations.

References:

1. Control differences between ac induction motor and brushless dc motor? Electrical Engineering Stack Exchange. Electronics.stackexchange.com (2019-12-20). Retrieved on 2019-12-26.
2. What is a BLDC Motor in a Washing Machine?". Dumb Little Man. Retrieved 11 June 2019.[3].T.G. Wilson, P.H. Trickey, "D.C. Machine. With Solid State Commutation", AIEE paper I. CP62-1372, October 7, 1962.
3. Jump up to:a b c d Clarence W. de Silva (2009). Modeling and Control of Engineering Systems. CRC Press. pp. 632–633. ISBN 978-1420076875.
4. Jump up to:a b c d Helmut Moczala (1998). Small Electric Motors. London: Institution of Electrical Engineers. pp. 165–166. ISBN 085296921X.
5. Jump up to:a b c d Chang-liang Xia (2012). Permanent Magnet Brushless DC Motor Drives and Controls. John Wiley and Sons. pp. 18–19. ISBN 978-1118188361.
6. "Custom axial flux permanent magnet BLDC". Turncircles. Retrieved 23 November 2020.
7. E. Levin, R. Gewirtzman, and G.F. Inbar, “Neural Network Architecture for Adaptive System Modellinggand Control”, Neural Networks, No 4(2) pp 185-191,1991.
8. A. Mamadapur and G. Unde Mahadev, "Speed Control of BLDC Motor Using Neural Network Controller and PID Controller," 2019 2^nd International Conference on Power and Embedded Drive Control (ICPEDC), 2019, pp. 146-151.
9. Xiong, G. Junguo, C. Jian and J. Biao, “Research on Speed Control System of Brushless DC Motor Based on Neural Network,” 2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA), Nanchang, 2015, pp. 761-764.