Industrial implementation of a fuzzy logic controller for brushless DC motor drives using the PicoMotion control framework

Ahmed, Sevil; Topalov, Andon V.; Dimitrov, Nikolay; Bonev, Eugene

doi:10.1109/is.2016.7737493

Cited by 7 publications

(3 citation statements)

References 8 publications

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“…It should be taken care that the membership functions overlap each other. This is done in order to avoid any kind of 268 discontinuity with respect to the minor changes in the inputs [17,18]. T2FLC consists of a set of membership functions (MFs) that operate with 3D uncertainties, whereas the MFs of type 1 fuzzy sets operate with only two dimensions.…”

Section: Type-1 Flc and Type-2flc (T1flc T2flc)mentioning

confidence: 99%

Type 1 versus type 2 fuzzy logic speed controllers for brushless dc motors

Abed

Humod

Humaidi

2020

IJECE

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This work presented two fuzzy logic (FL) schemes for speed-controlled brushless DC motors. The first controller is a Type 1 FL controller (T1FLC), whereas the second controller is an interval Type 2 FL controller (IT2FLC). The two proposed controllers were compared in terms of system dynamics and performance. For a fair comparison, the same type and number of membership functions were used for both controllers. The effectiveness of the structures of the two FL controllers was verified through simulation in MATLAB/SIMULINK environment. Simulation result showed that IT2FLC exhibited better performance than T1FLC. INTRODUCTIONBrushless DC (BLDC) motors can be used in several fields and applications, such as industrial automated electric vehicles and aerospace computers. BLDC motors exhibit several advantages over brushed DC motors. They are characterised by low maintenance due to commutator disposal, long operating life due to lack of friction and electrical losses and high power density [1,2]. The BLDC motors have no brushes, which prolongs the life time of motor and avoids their maintenance. Additionally, these motors are characterized by high electromagnetic torque-to-weight ratio, which make them suitable for most application [2,3]. Compared with brushed DC motors and induction machines, BLDC motors have lower inertia that enables faster dynamic response to reference commands. In addition, they are more efficient due to the permanent magnets that can perform with virtually zero rotor losses [3][4][5].BLDC motors have a complex and nonlinear model. To overcome the control problem, a nonlinear fuzzy logic controller (FLC) is used to control the speed of a BLDC motor. This intelligent controller has a simple structure and is relatively easy to implement due to its modest fuzzy rule in the rule base [6,7]. Recently, the intelligent control of BLDC motors has elicited the attention of many researchers. A review of the most relevant studies is presented in this paper. R. Arulmozhiyal and R. Kandiban compared a conventional proportional-integral-derivative (PID) controller and a fuzzy PID controller in terms of the speed control of BLDC motors. The results were obtained using MATLAB/SIMULINK and then experimentally verified.The simulated and practical results showed that the fuzzy PID.Controller outperformed the conventional PID controller [8]. E. Blessy and M. Murugan analysed a BLDC motor model and designed an FLC to improve the dynamic performance of speed control. They compared the fuzzy logic (FL), proportional, proportional-integral and PID controllers to evaluate the impact of each controller on speed dynamic performance [9].

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Section: Type-1 Flc and Type-2flc (T1flc T2flc)mentioning

confidence: 99%

Type 1 versus type 2 fuzzy logic speed controllers for brushless dc motors

Abed

Humod

Humaidi

2020

IJECE

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“…Fuzzy Logic Controllers (FLC) do not require an exact mathematical model, they are designed based on general knowledge of the plant [35]. Fuzzy logic control is a control algorithm based on a linguistic control strategy which tries to account the humans knowledge about how to control a system without requiring a mathematical model.…”

Section: Theoretical Design Of Fuzzy Logic Controllersmentioning

confidence: 99%

Performance comparison of a bridge-less canonical switching cell and H-bridge inverter with SVPWM fed PMBLDC motor drive under fuzzy logic controller

Joy¹,

Ushakumari²

2018

MMC_A

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In this paper, speed control, torque ripple minimization and power factor correction of a bridge-less canonical switching cell and H-bridge inverter with space vector PWM fed permanent magnet brushless DC motor drive system with varying load is discussed. The constant speed of operation with minimum torque ripples and unity power factor operation during transient state is the most difficult control part in the motor drive system. At the starting condition, the current is too high due to the absence of back EMF and therefore the motor will start with high torque ripples. In order to eliminate these torque ripples during starting condition by limiting the starting current of the motor, it is necessary to have properly designed bridge-less canonical switching cell converter, H-bridge inverter and the intelligent controller, which will improve the power factor of the AC supply system and reliability of the PMBLDC motor drive. The performance parameters of a PMBLDC motor drive with this inverter and controller are analyzed through MATLAB/ Simulink and the real-time implementation is validated with 42BLF01 PMBLDC motor drive system

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“…They showed that this was a great advantage, especially when looking for robot manipulators either in the industry performing repetitive tasks, or in service robots performing manipulation tasks. Nevertheless, despite the progress in this field, there is still an urgent need to minimize cost and time for certain robotic applications [9][10][11][12][13]. Robotic systems still require many improvements in design, accuracy, and real-time processing.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Computer-Aided Engineering Techniques for Robot Arm Applications

Zouari¹,

Chtourou²,

Ayed³

et al. 2020

Eng. Technol. Appl. Sci. Res.

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As systems become increasingly complex, their simulation techniques have to be more accurate and enhanced. Despite the wide use of robotic arms in industries, they still encompass a wide number of complexities. The control of a flexible robot arm driven by a Brushless DC Motor (BDCM) for tracking problems is a great challenge, not only for its complex algorithms but also for its verification process. Robotic systems are designed heterogeneously by combining continuous and event discrete models. Therefore, computer-aided engineering tools have to be enhanced in order to support the verification of the control algorithms. Ensuring definite and rapid simulations is a challenging task for robotic application systems. Although control strategies can be tested using the Matlab/Simulink environment to assess their performance, their verification at a low-level still remains a very difficult task. This paper studies different simulation techniques based on Model In the Loop (MIL), Hardware In the Loop (HIL), and Hardware Software In the Loop (HSIL) on a flexible robot arm driven by BDCM in order to overcome each method's limits, focusing on performance and cost and simulation time reduction. The HSIL achieves the highest accuracy and speed than MIL and HIL and provides reusability and portability of the control unit compared to the other techniques.

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Industrial implementation of a fuzzy logic controller for brushless DC motor drives using the PicoMotion control framework

Cited by 7 publications

References 8 publications

Type 1 versus type 2 fuzzy logic speed controllers for brushless dc motors

Type 1 versus type 2 fuzzy logic speed controllers for brushless dc motors

Performance comparison of a bridge-less canonical switching cell and H-bridge inverter with SVPWM fed PMBLDC motor drive under fuzzy logic controller

A Comparative Study of Computer-Aided Engineering Techniques for Robot Arm Applications

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