Self-tuning fuzzy PID speed controller for quarter electric vehicle driven by In-wheel BLDC motor and Pacejka's tire model

Baz, Rachida; Majdoub, Khalid El; Giri, F.; Taouni, Abderrahim

doi:10.1016/j.ifacol.2022.07.377

Cited by 18 publications

(9 citation statements)

References 13 publications

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“…Controller is composed of traditional increment PID controlling means and WNN, which is illustrated in Figure 1. Increment PID directly performs closed-loop control on the controlled object, and the three parameters K p , K i , K d of the controller are adjusted online, while the NN adjusts the PID control parameters timely according to working condition of controlling model [13].…”

Section: Analysis Of Wnn Pid Controlling Theorymentioning

confidence: 99%

Retracted: Research on wavelet neural network PID control of maglev linear synchronous motor

Mao

2022

IET Circuits, Devices & Syst

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Retraction: [Jun Mao, Jun Ma, Research on wavelet neural network PID control of maglev linear synchronous motor, IET Circuits, Devices & Systems 2022 (https://doi.org/10.1049/cds2.12136)]. The above article from IET Circuits, Devices & Systems, published online on 8 December 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Harry E. Ruda, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.

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Section: Analysis Of Wnn Pid Controlling Theorymentioning

confidence: 99%

Retracted: Research on wavelet neural network PID control of maglev linear synchronous motor

Mao

2022

IET Circuits, Devices & Syst

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“…By leveraging human understanding of the factory in the control design process and decision-making process, fuzzy control is based on fuzzy set theory, linguistic variables, and fuzzy inference [11] [12]. Fuzzy control systems consist of fuzzification, fuzzy rule base, fuzzy inference, and defuzzification, as shown in Figure 2 illustrates the general diagram of a fuzzy inference system [13].…”

Section: Fuzzy Controllermentioning

confidence: 99%

Innovative Fuzzy PID Algorithm: Advancing Quadcopter Control with Adaptive Precision

2024

AJCIS

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The integration of fuzzy logic techniques into PID controllers shows promising advantages in quadcopter control. By incorporating fuzzy logic, the PID controller achieves enhanced adaptability and robustness, resulting in more dynamic and resilient control performance. This is achieved through the automatic adjustment of PID parameters based on real-time system feedback, enabling a control strategy that closely resembles human reasoning. Overall, the combination of fuzzy logic and PID control offers significant potential for improving quadcopter control in various environmental conditions.

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“…Hall Effect Sensor is a transducer that can convert magnetic information into electrical signals for further processing of electronic circuits. This Hall Effect sensor is often used as a sensor to detect proximity (proximity), detect position (positioning), detect speed (speed), detect movement direction (directional) and detect electric current (current sensing) [13]- [15]. A transducer that varies its output voltage in response to a magnetic field.…”

Section: Hall Effect Sensormentioning

confidence: 99%

Design And Implementation of Three Phase Motor Driver For PWM-Based BLDC Motor Speed Control

Masdi¹,

Yanto²

2023

Proceedings of the 1st International Conference on Sustainable Engineering Development and Technological Innovation, ICSEDTI 20

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The Along with the times, the need for motors that have high efficiency, high torque, high and variable speed, and low maintenance costs has increased. one of these motors is a Brushless DC motor that uses electric commutation so that it has high efficiency and long operating time. Therefore, to meet the need for high efficiency, high torque, high and variable speed, and low maintenance costs, a Brushless DC motor (BLDC) or a Brushless AC motor (BLAC) is used. BLDC motors are widely used in industry compared to other types of motors, because BLDC motors have many advantages. But there is also a weakness of the BLDC motor, namely the difficulty in regulating the speed. With this condition the author is interested in making an innovation to overcome this problem, by making a three-phase motor driver as a BLDC motor control to regulate the rotation of the BLDC motor so that the speed can be varied. This three-phase motor driver consists of an Arduino Nano microcontroller and a three-phase inverter circuit that uses an IRF3205 MOSFET. The Arduino Nano microcontroller is used as a MOSFET ignition in a three-phase inverter circuit, the result is The success parameter of this research is being able to determine the commutation of the BLDC motor which is then controlled by a three-phase inverter through the Arduino NANO microcontroller to control the speed of the BLDC motor with a certain frequency.

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Self-tuning fuzzy PID speed controller for quarter electric vehicle driven by In-wheel BLDC motor and Pacejka's tire model

Cited by 18 publications

References 13 publications

Retracted: Research on wavelet neural network PID control of maglev linear synchronous motor

Retracted: Research on wavelet neural network PID control of maglev linear synchronous motor

Innovative Fuzzy PID Algorithm: Advancing Quadcopter Control with Adaptive Precision

Design And Implementation of Three Phase Motor Driver For PWM-Based BLDC Motor Speed Control

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