Performance Of Brushless DC Motor Research Articles

A Method of Anti-Windup PID Controller for a BLDC-Drive System

This research aims to enhance control systems for Brushless DC (BLDC) motors by introducing Proportional-Integral-Derivative (PID) control as a straightforward yet reliable solution, known for its precision, quick responsiveness, and stability. Emphasizing its suitability for BLDC motor speed control, the study addresses PID controller windup challenges, highlighting anti-windup techniques crucial for maintaining system stability. The primary focus is on improving the performance of an anti-windup PID controller for BLDC motor speed control in electric vehicles. Through simulations and analyses, the research aims to minimize steady-state error and overshooting, contributing to overall operational efficiency. Practical implementation involves optimizing the PID anti-windup controller's gain using the MATLAB PID Tuner and implementing it in the Arduino IDE. Experimental tests, which cover constant and varying step function scenarios, are conducted on the designed hardware. Results show the PID anti-windup controller's superiority, exhibiting significantly lower overshoot values of 5.42% and 3.35% compared to 13.26% and 23.76%, respectively. Despite its higher control action, the traditional PID controller displays a more pronounced overshoot. This research is a significant step toward advancing control systems for electric vehicles and optimizing BLDC motor performance in practical applications.

Analysis and Controlling of Uncertainty in BLDC Motor Using Optimal Hybrid Algorithm in State Space

In this article, we present a novel approach for enhancing the performance of Brushless DC Motors (BLDC) by utilizing a Hybrid Controller that combines Proportional – Integral – Derivative (PID) and Artificial Neural Network (ANN) elements. We employ this innovative controller to assess the motor’s behavior under steady-state conditions and internal faults within a state space model. To evaluate the controller’s effectiveness, we compare it with traditional controllers in scenarios involving rapid load changes, and variations in speed, as well as open circuit faults and short circuit faults, which are known to induce disruptive states in the motor. Our study involves a comprehensive examination of the proposed controller’s steady-state stability using state-space representation and the Lyapunov Technique. We construct a prototype model to capture the Steady State and dynamic characteristic variables of a 300 W BLDC motor system. The suggested Hybrid PID-ANN Controller clearly outperforms the traditional PID controller, providing greater control capabilities for BLDC motor applications depending on the results from both simulations and data from experiments.

Performance Analysis and Implementation of Brushless DC Motor by PID, Fuzzy and ANIFS Controllers

Now a day’s Brushless DC (BLDC) motors are more popular because of its good electrical and mechanical property. These motors are now changing to brushed DC motors and induction motors in various applications. The main problem that occurs in the traditional PID controller is that the parameters obtained from the drive control system of the BLDC servo motor cannot produce a steady state under various operating conditions such as adjusted gain plus transient response and parameter variation, load disturbance. So a fuzzy controller provides good speed response for start-up. In this paper how the design and simulation of ANFIS controller is better for the performance of brushless DC motor (BLDC) servomotor as well as the design and implementation of ANFIS controller and its performance are compared is shown. The capability of BLDC servomotor based on Fuzzy, ANFIS and PID controller is investigated under different operating conditions. Paper represents the PID controller and fuzzy controller to demonstrate error tracking capability and usefulness of ANFIS controller in control applications. By using the PID, Fuzzy and ANFIS controller we can implement, analyze the overall performance of brushless DC motor. KEYWORDS: Proportional-Integral-Derivative (PID) controller, Brushless DC (BLDC) motor, Adaptive-Network- Based Fuzzy Inference System (ANFIS), (FLC) Fuzzy logic controller (IGBT) Insulated Gate Bipolar Transistor.

Sensored speed control of brushless DC motor based salp swarm algorithm

<p>This article uses one of the newest and efficient meta-heuristic optimization algorithms inspired from nature called salp swarm algorithm (SSA). It imitates the exploring and foraging behavior of salps in oceans. SSA is proposed for parameters tuning of speed controller in brushless DC (BLDC) motor to achieve the best performance. The suggested work modeling and control scheme is done using MATLAB/Simulink and coding environments. In this work, a 6-step inverter is feeding a BLDC motor with a Hall sensor effect. The proposed technique is compared with other nature-inspired techniques such as cuckoo search optimizer (CSO), honey bee optimization (HBO), and flower pollination algorithm (FPA) under the same operating conditions. This comparison aims to show the superiority features of the proposed tuning technique versus other optimization strategies. The proposed tuning technique shows superior optimization features versus other bio-inspired tuning methods that are used in this work. It improves the controller performance of BLDC motor. It refining the speed response features which results in decreasing the rising time, steady-state error, peak overshoot, and settling time.</p>

Evaluation and Comparative Analysis of Speed Performance of Brushless DC Motor using Digital Controllers

Abstract: This paper presents modeling, performance evaluation, and comparative analysis of speed performance of brushless DC motor (BLDCM) by using digital controllers. Speed performance analysis is carried out by using time response specifications which are useful for determining the effectiveness of the digital controllers. The wide spread of BLDCM in many areas due to the advantages of BLDCM over the conventional widely used motors such as induction motor and brushed DC motor. Advantages of BLDCM include higher efficiency, lower maintenance, longer life, reduced losses, single excitation, etc. Controllers are used to improve the transient and steady state speed response of the BLDCM. In many applications conventional PID controller is widely used to control the speed of the BLDCM but the main issue with the conventional PID controller is that it requires manual tuning of the parameters such as proportional, integral, and derivative gain constant. Even though the autotuning methods are available with the PID controller it is not adaptive itself to handle the conditions such as variations in parameters, disturbances in load, etc. In this Paper the Fuzzy-PID controller is used to control the speed of the BLDCM and Transient and steady state speed performance analysis is carried out using conventional PID controller and Fuzzy-PID to showcase the comparative analysis between two controllers. MATLAB/SIMULINK environment is used for modeling of the BLDCM and its drive/control system. Keywords: Brushless DC Motor (BLDCM), Fuzzy Logic Controller (FLC), Modeling of BLDC drive/control system, of PID controller, Transient and steady state analysis

Optimal Coronavirus Optimization Algorithm Based PID Controller for High Performance Brushless DC Motor

This paper presents an efficient coronavirus optimization algorithm (CVOA) to find the optimal values of the PID controller to track a preselected reference speed of a brushless DC (BLDC) motor under several types of disturbances. This work simulates how the coronavirus (COVID-19) spreads and infects healthy people. The initial values of PID controller parameters consider the zero patient, who infects new patients (other values of PID controller parameters). The model aims to simulate as accurately as possible the coronavirus activity. The CVOA has two major advantages compared to other similar strategies. First, the CVOA parameters are already adjusted according to disease statistics to prevent designers from initializing them with arbitrary values. Second, the approach has the ability to finish after several iterations where the infected population initially grows at an exponential rate. The proposed CVOA was investigated with well-known optimization techniques such as the genetic algorithm (GA) and Harmony Search (HS) optimization. A multi-objective function was used to allow the designer to select the desired rise time, the desired settling time, the desired overshoot, and the desired steady-state error. Several tests were performed to investigate the obtained proper values of PID controller parameters. In the first test, the BLDC motor was exposed to sudden load at a steady speed. In the second test, the continuous sinusoidal load was applied to the rotor of the BLDC motor. In the third test, different operating points of reference speed were selected to the rotor of the BLDC motor. The results proved that the CVOA-based PID controller has the best performance among the techniques. In the first test, the CVOA-based PID controller has a minimum rise time (0.0042 s), minimum settling time (0.0079 s), and acceptable overshoot (0.0511%). In the second test, the CVOA-based PID controller has the minimum deviation about the reference speed (±4 RPM). In the third test, the CVOA-based PID controller can accurately track the reference speed among other techniques.

Implementation of SPV-powered water pumping system using non-isolated SC converter topology

Plentiful accessibility of fuels, non-erratic, reliable performance, low maintenance, low cost and high life are the significant factors for solar-PV-powered water pumping scheme for irrigation applications. It is highly used in irrigation system for cultivation where the utility-grid is non-available, hilly areas, forests, etc., by utilizing solar source with energy conversion methodology. However, the performance of overall irrigation scheme relies on energy conversion methodology by using feasible DC–DC converter. In this paper, a novel non-isolated switching cell type DC–DC converter has been proposed for acquiring greater performance of Brushless-DC motor to lift the water from ponds or canal through submersible pump. This observation is clearly presented and validated under steady-state condition by using Xilinx FPGA SPARTAN-6 hardware prototype; results are carried out and compared with various converter topologies.

Expansion of Operating Speed Range of High-Speed BLDC Motor Using Hybrid PWM Switching Method Considering Dead Time

In vehicle electrical systems with limited battery power, the output torque and speed of high-speed brushless DC (BLDC) motors can decrease due to unstable and reduced supply voltage or manufacturing errors in the motor back electromotive force (EMF). This paper presents a method that can guarantee the output performance of an inverter through a control algorithm without a separate power supply system and DC-link voltage increase. The proposed control algorithm can increase the output torque and speed of a high-speed BLDC motor by using appropriate selection and change of the inverter’s pulse width modulation (PWM) control method. In this paper, the operation and electrical characteristics of various PWM methods of BLDC motors are analyzed, and the optimal PWM method for improving the control performance of high-speed BLDC motors is presented. In addition, the relationship between the switching frequency, dead time, and voltage utilization was mathematically analyzed. Based on the results of this analysis, the proposed control algorithm automatically changes the PWM switching mode at the point where the output torque and speed need to be extended. The effectiveness and feasibility of the control method proposed in this paper is verified through the experimental results on the designed and manufactured high-speed BLDC motor system for vehicles.

Performance Analysis of BLDC Motor Drive using Enhanced Neural Based Speed Controller for Electric Vehicle Applications

This paper deals with Brushless DC (BLDC) motor drive mathematical model with hysteresis current controlled based Voltage Source Inverter (VSI) which operates using Neural Network (NN). The developed simulation model is applied with the artificial neuro speed controller which is based on Least Mean Square (LMS) and Adaptive Linear Neuron (ADALINE) to improve the performance of BLDC motor drive. Under dynamic operating conditions, the NN speed controller is trained by the data obtained from closed loop speed controller of BLDC motor drive system. The developed conventional and proposed simulation models are simulated using MATLAB/Simulation software. The proposed neural based speed controller is important for tracking of motor speed as well as torque with their reference values with minimum transient time. In this paper ADALINE network model is developed for update of weights in NN. Here, a comparative study has been done for PI and NN controller for BLDC drive. The simulation results show that the NN based speed controller is more effective controller compared to classical PI based speed controller during most of the dynamic operating conditions.

Modification of speed and current limiting performance of BLDC motor based on harmony search optimization method

The brushless DC (BLDC) motor is a multi-variable and non-linear system so that it's required more advanced explaining and controlling set. In this work, an algorithm is proposed based on the harmony search algorithm (HAS) method. HSA mimics music improvisation process to find the optimal solution. In BLDC motor, many constraints are required; speed performance and current limiting control are dominated. HAS is proposed to find the optimal PID parameters for the two speed and current-limiting controllers that are proposed in the drive. By Matlab/Simulink results, a good performance is shown compared with the classical method that absence current-limiting controller.

Intelligent fuzzy sliding mode controller based on FPGA for the speed control of a BLDC motor

Brushless DC (BLDC) motors are one of the most widely used motors for various industrial applications due to their high efficiency, high torque to weight ratio and elimination of mechanical commutator. These motors operate in wide range of speeds and necessitate precise speed control techniques, for their nonlinear model, insenseitive to parameter variations and external disturbances, when used in various sensitive applications. Conventional PI and other existing controllers produce high overshoot and increased rise time and settling time. The performance of BLDC motor is enhanced using a Fuzzy Sliding Mode Controller (FSMC) whose gain is intelligently varied with the help of a Fuzzy Inference System (FIS). For this purpose, a suitable FSMC is designed, simulated and implemented using FPGA. The simulation results are validated using Hardware in the loop (HIL) simulation as well as actual hardware implementation. Great improvement in the transient performance is achieved when compared to chatter free SMC, Fuzzy PI and conventional PI controller.

Rapid estimation and compensation method of commutation error caused by Hall sensor installation error for BLDC motors

Hall sensors provide significant commutation signals for brushless DC (BLDC) motors. However, installation errors of Hall sensors are almost inevitable for BLDC motors, which can lead to commutation errors, further current ripples and torque ripples. Meanwhile, abnormal diode conducting in inactive phase can be aroused by large commutation errors at high speed. Therefore, the commutation errors make a negative influence on motor efficiency and degrade the performance of BLDC motors. Here, a simple and fast method to estimate the commutation error is proposed. Based on the analysis of the commutation process under commutation error, only the turn-off phase current at the commutation instant is required for the estimation of delayed commutation error, while only the time interval of the turn-off phase current decreasing to zero after commutation instant is needed when advanced commutation occurs. In order not to affect the dynamic performance of a BLDC motor, this study utilises a straightforward compensating method of commutation error. The experimental results represent that the proposed method is effective at different speeds, load torques, and under load-variation with good transient performance.

Performance evaluation of electric vehicle brushless direct current motor with a novel high-performance control strategy with experimental implementation

In this article, a new control strategy for electric vehicle battery autonomy and performance improvements is proposed. This method is based on the use of a DC-DC converter combined with a brushless DC motor as an electric vehicle propulsion engine. First, an analysis of the three-phase brushless DC motor performance based on analytical modeling is addressed. This model stands on the derivative of the commutated and non-commutated phase currents from which the speed response is induced. The proposed model presents different brushless DC motor pulse width modulation control modes and allows highlighting the most appropriate mode with lower current ripple and power loss, higher efficiency and faster response. Furthermore, a DC-DC boost converter is integrated in the motor control scheme, to describe after, how the electric vehicle performance is increased when implementing this control mode. Evaluation of the analytic results has been validated through simulations on MATLAB/Simulink. The evaluation focuses on speed response for different pulse width modulation control modes, current ripple rate in addition to the electric vehicle performance with and without DC-DC converter. In addition, an experimental validation using a laboratory designed platform is performed. The proposed method demonstrates a better electric vehicle performance in terms of increasing the battery autonomy and speed range.

Efficient dynamic performance of brushless DC motor using soft computing approaches

A novel attempt to employ moth swarm algorithm (MSA) to generate the optimal gains of a proportional–integral (PI) speed controller of brushless DC (BLDC) motor is addressed to assure its satisfactory dynamic performance. For torque ripples minimization, a dual-loop speed controller is adapted. The agreed objective function is formulated to minimize the integral time absolute speed error (ITAE) subjects to set of constraints. The effectiveness of the MSA is verified through many test cases along with the detailed comparisons to those obtained by well known genetic algorithm and particle swarm optimization. At this stage, the numerical results of the MSA are used to train and test an artificial neural network which shall be used as an adaptive controller to give the optimal PI gains under different operating conditions. At final stage, the performance of the BLDC motor powered from photovoltaic (PV)–battery hybrid system with the proposed controller is demonstrated. A Landsman converter is controlled by an incremental conductance technique to maximize the extracted PV array power. A bidirectional converter is used to control battery charging/discharging states. Various demonstrated case studies indicate that the MSA is effective in generating the optimal gains of the PI controller.

A Novel Fuzzy Self Tuning Technique of Single Neuron PID Controller for Brushless DC Motor

In this paper, a combination ANN/Fuzzy technique is used to design a Novel Fuzzy Single Neuron PID (NFSNPID) controller to achieve high performance brushless DC motor. The design steps include two parts. The first part uses the genetic algorithm (GA) to find the optimum parameters of Single Neuron PID (SNPID) controller, while the former deals with the design of fuzzy logic control to update the weights of SNPID control online. To demonstrate the designed controller effectiveness, a comparative study is made with between the NFSNPID, Conventional Fuzzy Single Neuron PID CFSNPID and SNPID. All controllers were used to drive, separately, the brushless DC motor against the sudden change of load and operating speed. The performed simulations show better results that motivate for further investigations.

Power Factor Correction with Current Controlled Buck Converter for BLDC Motor Drive

Brushless DC motor is a synchronous machine that makes use of electronic commutation instead of mechanical commutator. Brushless DC motors makes use of inverter encompassing static switches for its operation. A simple bridge converter when used for BLDC drive as front end converter makes input source power factor to get reduced which is unacceptable in the power system. To avoid the distortions in the source voltage and source currents, Buck converter which was used as power factor correction (PFC) converter in this paper to improve the power factor. Presence of power electronic converters deteriorates system power factor effecting overall system performance. This paper presents buck converter for power factor correction in brushless DC motor drive system. Buck converter is operated with current control strategy rather to conventional voltage follower control. Simulation model was obtained using MATLAB/SIMULINK software and the brushless DC motor performance characteristics were shown for conditions with different DC link voltages and step variation in DC link voltage. Total harmonic distortion in source current was also presented.

Impact Analysis of PM magnetization level on motor performance: simulations and experimental results

This paper addresses the impact of magnetization level tolerances in ferrite-based permanent magnets (PM) on the performance of brushless DC motors. A case study is prepared using three motor prototypes assembled with controlled samples of ferrite arc segments, in terms of magnetization. The investigation is performed both experimentally and through simulations using the software SPEED. The results show that narrow tolerance margins for the magnetization conditions in the production line can potentially minimize the use of raw material on such motors whereas keeping the efficiency and torque behavior required by the application.

Brushless DC motor tracking control using self-tuning fuzzy PID control and model reference adaptive control

Abstract This paper compares the performance of two different control techniques applied to high performance brushless DC motor. The first scheme is self-tuning fuzzy PID controller and the second scheme is model reference adaptive control (MRAC) with PID compensator. The purpose of the control algorithm was to force the rotor speed to follow the desired reference speed with good accuracy all time. This objective should be achieved for different speed/time tracks regardless of load disturbance and parameter variations. The simulation results presented show that the second control scheme has better performance.

Study on radiation induced performance degradation of BLDC motor in robot servo systems

Humanoid robots have great potential in applications in organically dangerous environments, for example, in the radiation environments. However, the radiation damage on robot components except for electronic circuits has not been well studied. Our study in this paper is the first on the radiation induced performance degradation of the brushless DC (BLDC) motor in the robot servo systems. Based on the fact that radiation causes NdFeB magnet demagnetisation, we analyse how this demagnetisation would affect the BLDC motor performance by establishing a model introducing the demagnetisation factor into the electromagnetic model of the BLDC motor, and simulated the motor operation under open loop PWM control in MATLAB. The result shows that the radiation demagnetised BLDC motor will decrease in power, lose controllability, and even quit working by a 'magnetic-trap' jerking effect.

Experimental Research on Regenerative Braking of Wheel-Hub Motor

The factors affecting regenerative braking performance of brushless DC motor(BLDCM) were analyzed by means of bench test and road test .the BLDCM was used as drive motor in a four –wheel-independent-drive EV. The relation between PWM duty and motor rotation speed, charging current and voltage were determined by test, which can benefit design of control strategy of regenerative braking for electric motor and help to improve energy efficiency.