Sensorless Start-Up Control for BLDC Motor using Initial Position Detection Technique

Brushless direct current (BLDC) motor has been widely used in industrial production. Based on the method of model design, BLDC motor speed control is studied. In Matlab/Simulink, a fuzzy PID speed control model of BLDC motor is established, and C Code is generated by Real-Time Workshop (RTW) technology. After that, Code Warrior is compiled into machine Code and written into ECU. The results show that the fuzzy Proportion Integration Differentiation (PID) controller can improve the speed performance of the BLDC.

This paper proposes a design of optimal PI(D) controller for brushless DC (BLDC) motor speed control by the particle swarm optimization (PSO), one of the powerful metaheuristic optimization search techniques. The proposed control system is implemented on the TMS320F28335 DSP board interfacing to MATLAB/SIMULINK. With Back EMF detection, the proposed system is considered as a class of sensorless control. This scheme leads to the speed adjustment of the BLDC motor by PWM. In this work, the BLDC motor of 100 watt is conducted to investigate the control performance. As results, it was found that the speed response of BLDC motor can be regulated at the operating speed of 800 and 1200 rpm in both no load and full load conditions. Very satisfactory responses of the BLDC system can be successfully achieved by the proposed control structure and PSO-based design approach.

This paper presents a notion of speed control of brushless DC (BLDC) motor through a compound control technique using CMAC (cerebellar model articulation controller) and PID controller. The proposed system provides effective starting and diminished cost and core loss. Compound controller realizes the dual-fed control which ensures stability and convergence of system. The initial rotor position is determined by estimating the voltage on sample resistor moreover the core loss are significantly diminished by using the ferrite material in stator core. The benefits like high control accuracy, increased efficiency, adaptability, decreased startup time and increased starting torque are attained in presented system.

This study presents the design and development of a tire tread with a brushless DC (BLDC) motor speed control and monitoring system for an unmanned ground vehicle (UGV) in mining applications. By providing precise speed control and real-time monitoring of the motor's operating parameters, the proposed system seeks to enhance the performance of UGVs used in mining operations. A BLDC motor, a motor controller, and a monitoring system that captures data on the motor's speed, temperature, and voltage are incorporated into the tire tread design. The data is wirelessly transmitted to a central control system, enabling operators to monitor the UGV's performance and make informed maintenance and operation decisions. Experiment results demonstrate the efficacy of the proposed system in enhancing the performance and safety of UGVs on mining sites, highlighting the system's potential for utilization in other industries.

This paper introduces a good method which is helpful to assist in the design and control of cost effective, efficient Brushless Direct Current (BLDC) motors. Speed Control of BLDC motor using PIC microcontrollers requires more hardware, and with the availability of FPGA versatile features motivated to develop a cost effective and reliable control with variable speed range. In this paper, an algorithm which uses the Resolver signals captured from the motor is developed with the help of Resolver to Digital converters. The program has been written using VHDL. This program generates the firing pulses required to drive the MOSFETs of three phase fully controlled bridge converter driven by drivers. Then the program has been loaded on the Spartan- 3 FPGA device and tested on the 30V, 2000 rpm BLDC motor which can make the motor run at constant speed ranging from 10 to 2000 rpm. The proposed hardware and the program are found to be very good and efficient. The results are good compare to PIC Microcontroller based design.

This paper presents a structured and experimentally validated approach to the parameter identification, modeling, and real-time speed control of a brushless DC (BLDC) motor. Electrical parameters, including resistance and inductance, were measured through DC and AC testing under controlled conditions, respectively, while mechanical and electromagnetic parameters such as the back electromotive force (EMF) constant and rotor inertia were determined experimentally using an AVL dynamometer. The back EMF was obtained by operating the motor as a generator under varying speeds, and inertia was identified using a deceleration method based on the relationship between angular acceleration and torque. The identified parameters were used to construct a transfer function model of the motor, which was implemented in MATLAB/Simulink R2024b and validated against real-time experimental data using sinusoidal and exponential input signals. The comparison between simulated and measured speed responses showed strong agreement, confirming the accuracy of the model. A proportional–integral (PI) controller was developed and implemented for speed regulation, using a low-cost National Instruments (NI) USB-6009 data acquisition (DAQ) and a Kelly controller. A first-order low-pass filter was integrated into the control loop to suppress high-frequency disturbances and improve transient performance. Experimental tests using a stepwise reference speed profile demonstrated accurate tracking, minimal overshoot, and robust operation. Although the modeling and control techniques applied are well known, the novelty of this work lies in its integration of experimental parameter identification, real-time validation, and practical hardware implementation within a unified and replicable framework. This approach provides a solid foundation for further studies involving more advanced or adaptive control strategies for BLDC motors.

As solutions to the problem of flux-weakening in brushless DC (BLDC) motor, sophisticated control methods and high switching frequency in flux-switching permanent magnet motors, this paper presents a novel parallel permanent magnet flux-switching motor, which inherits the advantages of permanent magnet motor, e.g. high power and torque density, and good flux-weakening capability. Based on motor topology, this paper makes a detailed analysis on the motor flux-linkage and the back EMF. The flux-weakening capability of motor is analyzed on the basis of the circuit model of the brushless DC motor when operating. Finite element simulation results prove the motor back EMF is trapezoidal waveform, and it is suitable for brushless DC occasions. Moreover, MATLAB simulation proves the motor has a good performance in flux-weakening.

Electric vehicles are currently being developed with the concept of saving energy and environmentally responsible. Electric vehicles use electric motors as the main driving motor. The electric motor which is currently increasingly used is the Brushless Direct Current (BLDC) motor. The BLDC motor controller is more expensive than the price of the BLDC motor itself. The BLDC motor controller has a working temperature range when working. The purpose of this study was to analyze the effect of variations in load values and the speed of electric vehicles on the temperature of the BLDC motor controller. This research was conducted by the experimental method by giving a treatment, then evaluating the effect caused on an object of research. The results showed that there was an influence of load value variations on the BLDC motor controller temperature. Values that look significant are at speeds of 0.5 m/sec and 2.5 m/sec starting from 3 kg to 5 kg. There is no influence on the variation of speed values on the temperature of the BLDC motor controller. The temperature value is stable at 2 kg and 3 kg loads with speed variations from 0.5 m/sec to 2.5 m/sec. Simultaneously variations in load and speed values affect the temperature of the BLDC motor controller by 57.7% with the lowest temperature value at 0.5 m/s and a load of 1 kg with a temperature of 33.34℃ and the highest temperature value at a speed of 2.5 m/sec and a load of 5 kg with a temperature of 37.58℃. Keywords: Electric vehicles, controllers, BLDC motors, temperature.

The advancements in the field of materials and force gadgets, coupled with the increased affordability of highperformance processors, have led to a widespread adoption of Brushless Direct Current (BLDC) motors across various industries. These applications range from household appliances to automotive, aerospace, and medical sectors. The widespread use of may be attributed to its manifold advantages over various types of engines, such as its superior efficiency, robust power output, extended operational lifespan, relatively quiet operation, and broader range of achievable speeds. Due to the increasing adoption of Brushless Direct Current (BLDC) motors in many real-life applications as a replacement for traditional motors, this paper aims to compile and evaluate a comprehensive list of control algorithms employed for BLDC motor control. This study discusses several ways for managing speed and current, including hysteresis band control, variable DC-link voltage control, and Pulse Width Modulation (PWM) control schemes. The optimization of Proportional-Integral-Derivative (PID) gains for these controlling techniques is achieved through the utilization of the particle swarm optimization (PSO) algorithm. By employing Fast Fourier Transform (FFT) analysis to examine the controller behavior through frequency analysis of the output signals and calculating the Total Harmonic Distortion (THD), a more advantageous control method may be determined.

Initial rotor position detection is important to ensure the start-up operation of sensorless permanent magnet synchronous motor (PMSM) drives. In order to suppress the annoying noise caused by the additional signal injection during the detection process in the existing methods, a pseudorandom high-frequency (HF) square-wave voltage injection based low-noise initial position detection method is proposed in this article. Two fixed HF square-wave voltage signals are injected into the estimated d -axis randomly. As the magnetic pole position is detected, the magnetic polarity can be identified at the same time, based on the accumulation of the induced random HF current peak pairs without any extra signal. In addition, a saturated peak current delay compensation strategy is proposed to reduce the digital delay effects on the magnetic polarity detection. The proposed method makes the entire detection process simpler and faster. Furthermore, power spectral density (PSD) analysis of random HF currents is adopted to provide the theoretical support for noise reduction. Finally, the proposed method is verified by experiments on a 2.2-kW interior PMSM drive platform.

Brushless Direct Current (BLDC) motor is superior to carbon brushed DC motors, such as high efficiency, more speed range, less maintenance, longer life etc. This is because BLDC motor has electronic commutation and thus, does not have to overcome friction unlike the brushed DC motor. Thus, they find application in many areas such as Electric Vehicles and Robotics. However, a disadvantage of BLDC motor is that it requires a controller for electronic commutation which can be expensive and also increases the complexity of the implementation. The BLDC motors which are controlled by classical controllers suffer from problems of parametric variation during commutation and torque ripple in the output. The project aims to design a Robust controller for BLDC motor speed control so as to improve the output of BLDC motor.

Analysis of the demagnetized permanent magnets in BLDC motor is presented in this paper. Automotive, aerospace, consumer, medical and industrial applications are the fastest growing markets for Brushless DC (BLDC) Motor. BLDC motor has high efficiency, high power density, and low maintenance cost. Still, due to overloading the motor, lots of faults can occur in stator, rotor, bearings and permanent magnets. In this paper, focus has been given on faults in permanent magnets. Modeling of demagnetized BLDC motor is presented using MATLAB/SIMULINK and AWT (Analytic Wavelet Transform) analysis is also done to find time frequency ridges. Test results show that stator current peak increases as magnets are demagnetized.

A new method for optimizing the fuzzy PID controller, based on an artificial electric field algorithm (AEFA), is proposed in this paper, aiming at improving the stability indicator of the Brushless DC (BLDC) motor for the small satellite attitude control flywheel. The BLDC motor is the basic part of the small satellite attitude control flywheel. In order to accurately control the attitude of the small satellite, a good motor control system is very important. Firstly, the mathematical model of the BLDC motor is established and the BLDC motor speed control system using traditional PID control is designed. Secondly, considering that the small satellite speed control system is a nonlinear system, a fuzzy PID control is designed to solve the shortcomings of the fixed parameters of the traditional PID control. Finally, we find that the control accuracy of the fuzzy PID control will change with the range of the input. Therefore, we introduce the AEFA to optimize fuzzy PID to achieve high-precision attitude control of small satellites. By simulating the BLDC motor system, the proposed fuzzy PID controller based on AEFA is compared with the traditional PID controller and the fuzzy PID controller. Results from different controllers show that the proposed control method could effectively reduce steady state error. In addition, the proposed fuzzy PID–AEFA controller has the better anti-jamming capability.

This paper proposes the artificial intelligence (AI)-based optimal PID controller design optimization of brushless direct current (BLDC) motor speed control with phase advance approach. The proposed control system allows the speed adjustment of the BLDC motor by phase advance technique. In this paper, two selected AI algorithms, i.e., the adaptive tabu search (ATS) and the intensified current search (ICS) are conducted as the optimizer for the PID controller design. The proposed control system is simulated by MATLAB/SIMULINK. Results obtained by the ATS and ICS will be compared with those obtained by the Ziegler-Nichols (ZN) tuning rule and the genetic algorithm (GA). It shows that the speed response of the BLDC motor by phase advance with the PID controller optimized by the ICS outperforms better than the ZN, GA and ATS.

Brushless Direct Current (BLDC) Motor is an electromagnetic device that converts electrical energy into mechanical energy. BLDC motor is a type of motor used in electric cars and does not need brush for magnetic field replacement (commutation), but is carried out electronically commutated. In BLDC motor, temperature in the motor play important role on motor performance such as maximum power and efficiency. In this research the development of liquid cooling of BLDC motor is described. Development starts from the design, computation using software and the realization of the motor design results. The construction of a brushless DC motor is very similar to that of an ac motor, known as permanent magnet synchronous motor. The stator consisting of a core and copper windings forming a core and armature unit. There are 2 identical stators to form the BLDC.