Figure 3 - uploaded by Iraj Faraji Davoudkhani
Content may be subject to copyright.
Source publication
To precisely control the speed of BLDC motors at high speed and with very good performance, an accurate motor model is required. As a result, the controller design can play an important role in the effectiveness of the system. The classic controllers such as PID are widely used in the BLDC motor controllers, but they are not appropriate due to non-...
Contexts in source publication
Context 1
... complete block diagram of speed control of BLDC motor is shown in Fig. 3. This control structure consists of two control loops. The inner loop synchronizes the inverter gates signals with the electromotive forces. The outer loop controls the motor's speed by varying the DC bus voltage. The inverter which is connected to the dc supply feeds controlled power to the motor. The magnitude and frequency of the ...
Context 2
... complete block diagram of speed control of BLDC motor is shown in Fig. 3. This control structure consists of two control loops. The inner loop synchronizes the inverter gates signals with the electromotive forces. The outer loop controls the motor's speed by varying the DC bus voltage. The inverter which is connected to the dc supply feeds controlled power to the motor. The magnitude and frequency of the ...
Similar publications
Research of many topics in intelligent transportation system (ITS) is the essential study in automobile domain. Adaptive cruise control (ACC) is one of the advanced topics. This paper describes the longitudinal control of the vehicle by using fuzzy logic based approach. The purpose of this paper is to design Fuzzy logic based Adaptive cruise contro...
Citations
... Various researchers have used many controlling techniques to regulate the speed of the BLDC motors. F. Davoudkhani and M. Akbari [12] compared the BLDC motor speed regulation methods among PID, Fuzzy type-1, and Interval Fuzzy Type 2 with PID controller (IT2FLPIDC). The results presented the superiority in response to the IT2FLPIDC over the other controlling techniques. ...
... A novel control method that performs convergence and divergence among numerical units is the artificial neural network. Fuzzy and neural network techniques were combined to create a cutting-edge approach that incorporated the best aspects of both approaches and significantly improved nonlinear mapping, modeling, learning, and pattern realization [15]. ...
... BLDC motors provide several benefits over conventional motors, including more control flexibility, better torque capability, quieter performance, higher efficiency, smaller size and volume, and reduced fuel consumption [3,4]. On the other hand, closed-loop speed control is critical for some applications where the speed must be appropriately managed and modified regardless of load variations [5]. Many control techniques for controlling the speed of BLDC motors have been developed in recent decades. ...
A Brushless Direct Current Motor (BLDC) is a synchronous motor with trapezoidal/square wave counter-electromotive force, a typical example of highly coupled nonlinear systems. In industrial control, BLDC motor drive usually uses a proportional-integral (PI) controller to control the speed, but it is challenging to adjust the scale factors. In this paper, A novel Turbulent Flow of Water-based Optimization (TFWO) approach, inspired by how water travels in nature, is presented and implemented to control the speed of a BLDC motor by optimizing the PI controller. The speed controller is modified using the Integral of Squared Error (ISE) as the objective function, a time-domain performance index. The proposed optimization technique is compared to other nature-inspired optimization methods, such as Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Biogeography-Based Optimization (BBO) algorithms, to see its success in optimizing. It has been shown that the suggested technique for optimum adjustment of PI controller coefficients, which is to be positioned in the BLDC motor speed control loop, outperforms all existing algorithms in terms of accuracy and efficiency. The results show that the suggested control technique offers a quicker response time than other control systems and a considerably enhanced response time to fluctuations.
... Wang, H., P. etc. [16] Fuzzy adaptive PID Controller methods; The proposed control method can increase the effectiveness of BLDC motor, where this control can be a solution to several problems that are difficult to overcome with conventional methods. Davoudkhani, I.F. and Akbari, M. [17] Interval Type-2 Fuzzy Logic based PID Controller (IT2FLPIDC) methods of BLDC motor; IT2FLPIDC can solve problems of uncertainty and variations in working conditions. The test results of the proposed system were then compared with the PID and Type-1 methods, and the results showed that IT2FLPIDC provided superior performance and response compared to the two methods. ...
... A permanent magnet motors are called a BLDC motors with a smooth trapezoidal back electromagnetic field (EMF) wave shape. A BLDCM is indeed a revolving electrical system with the classical three-phase stator identical to that of the induction motor (IM) [13], [14]. That rotor holds constant magnets as have seen in Figure 1. ...
The constant innovative advancements of brushless DC motors (BLDCMs) have discovered a wide scope of utilizations. For instance, underground electric vehicles, drones, and submerged bikes have just confirmed elite BLDCMs. Be that as it may, their appropriation requires control frameworks to screen torque, speed and other execution attributes. This paper presents numerous brilliant controllers structure and order line programming to assemble, alter and mimic intelligent to control BLDCMs. Recently planned graphical user interface (GUI) structure for Multi controllers: conventional proportional integral derivative (PID) controller, intelligent controller based fuzzy techniques: type 1, type 2, and modified type 2 fuzzy logic controller (T1FLC, IT2FLC, and MIT2FLC respectively). Different phases of the problematic framework configuration process, from the underlying depiction to the last usage, can be acquired from the altered tool compartment. MATLAB Ver.2019b was utilized to mimic and plan all procedure GUI. The agreeable aftereffects of MIT2FLC have been approved also; the examination gives the subtleties of the GUI program through estimations, shapes, flowcharts and code, giving understanding into the presumptions of common-sense boundaries that may emerge in such a minimal effort mentor for BLDCM. At last, the outcomes acquired through a few reproduction tests affirm the legitimacy of the showed math model and designing of intelligent controllers. Keywords: Brushless DC motor Conventional PID controller Fuzzy logic controller Graphical user interface Multi intelligent controller This is an open access article under the CC BY-SA license.
... PWM control has low speed response, high switching losses, and Radio frequency interference, comparatively more expensive than other controls. The classic PID controllers are the most efficient and traditional method but they can't tune these non linear uncertainties because of its linear characteristics and, hunting's or oscillations, slow speed response are the major drawbacks of these controllers [5]. Non linear control method can deal with the non linear model of BLDC motor and time varying parameters of the system, but it is more sensitive to parameter variations, proof of stability is difficult (step response is unstable). ...
... Non linear control method can deal with the non linear model of BLDC motor and time varying parameters of the system, but it is more sensitive to parameter variations, proof of stability is difficult (step response is unstable). So by introducing the Artificial Intelligent controls such as Type1 fuzzy logic control and Type2 fuzzy logic control the stability of the controller can be increased [5]. Type 1 fuzzy PID Controller can work with any type of imprecise, distorted inputs and its construction is simple and easily understandable. ...
... 3dimensional disturbances and uncertainties can be controlled by it. The contrast between type 1fuzzy and type 2 fuzzy is related with the nature of membership functions [5]. Type 2 fuzzy logic system is associated with two type 1 fuzzy membership functions ie;upper and lower membership functions. ...
This paper proposes an intelligent speed control system for PMBLDC motor drive applications. A fuzzy sliding mode controller is designed for the high speed control of BLDC motors, Efficacy of the system can be improved by FSM controller. Due to the non- linear characteristics of BLDC motors PID controllers are not relevant to it. Noise disturbances, harmonics, and antecedent or consequent uncertainties are difficult to handle by Interval Type2 fuzzy PID controller. Whereas Sliding Mode Controller is most attractive control technique for all type of uncertainties, and improves the performance of the system. An effective Mat-lab / Simulation result presents the attractive performance of proposed system.
... Motor (Davoudkhani and Akbari 2016;Ontiveros-Robles et al. 2018), and this case is a nonlinear problem with relevance in industrial applications. In a real application, the speed is measured by an encoder and is controlled by the duty cycle of a pulse-width modulation (PWM) digital output. ...
At present time, general type-2 fuzzy logic provides better uncertainty modeling when compared to interval type-2 fuzzy logic, and this has been shown with the improvement in the performance of fuzzy logic controllers and fuzzy edge detectors. However, higher computational cost represents a limitation for many applications and implementation platforms, and it is because of this that the computational cost reduction is the main goal of this paper. The aim of this work is to model a GT2 FLS based on shadowed type-2 fuzzy sets (ST2 FS) and apply this approach to control problems. The ST2 FS consists of approximating the secondary membership function by shadowed sets and is because of this, that we present an analytic approach to obtain the shadowed set for triangular and Gaussian membership functions for any parameters. Based on this, we achieve a representation of the ST2 FS as a parameterized function, facilitating its implementation. The results are compared against the performance when a generalized type-2 fuzzy inference system is approximated using α-planes for control applications.
... Brushless Direct Current Motor (BLDC) has been a favourite motor in industry and transport due to high power density , efficiency and low maintenance cost [1][2][3]. A BLDC motor's rotor is made of permanent magnet and the number of pole pairs can vary from two to eight with alternate north (N) and south (S) poles. ...
Brushless Direct Current (BLDC) motors have gained popularity in recent years due to their high-power density. Many type of speed controller techniques have been developed and Proportional Integral Derivative (PID) controller has been the most widely used. However, PID’s performance deteriorates during nonlinear loads conditions. Over the past five years, controllers have been developed to overcome this limitations in BLDC speed control, however the solutions are focusing on forward motoring only. In this paper, a speed controller for BLDC with seamless speed reversal using Modified Fuzzy Gain Scheduling is proposed. The proposed controller regulates the speed using Fuzzy Gain Scheduling 49 base rules. The controller was tested for six test cases and compared to PID and Self-Tuning Fuzzy PID controller. It is found out the proposed controller yields lowest steady state error, ess of 0.025 % during step-changing speed test case. Overall, Modified Fuzzy Gain Scheduling BLDC speed controller outperforms the other two similar controllers in variable speed conditions. The controller has potential to be used as bidirectional drive in highly dynamic load conditions.
In recent years, electric vehicles (EVs) have attracted a lot of attention and are rapidly growing trends today due to their ability to compete with and overtake fossil fuel-powered vehicles among all electrified transportation tools. The primary focus of EV evolution is on improving driving range and optimizing energy consumption, both of which are essential demands achieved through improved and developing motor performance. The brushless direct current (BLDC) motor is one of the most superior choices for dynamic applications in EVs, providing the highest power density and capability of any motor. BLDCs are gaining popularity in EVs not only for their high performance in speed and position controls but also for meeting the requirements of smooth torque maintenance with torque ripple mitigation. This paper provides an overview of various advanced control strategies, such as field-oriented control (FOC), direct torque control (DTC), intelligent control (IC), controlling input voltage (CIV), current shaping techniques (CST), model predictive control (MPC), and sliding mode control (SMC). These strategies are used to regulate the torque ripples produced in BLDC motors, aiming to achieve energy-efficient EV performance for various applications. Additionally, it describes a cloud-based control system coupled with electric motor drives to address challenges in predicting current vehicle conditions for improved power distribution between the motor and battery systems. Furthermore, recent concerns and difficulties in advancing torque ripple mitigation control strategies are highlighted, with comparisons and discussions for future EV research. Finally, an evaluative study on BLDC motor drive in EVs with different control strategies reveals its significance and potential outcomes.
