Log in

Relevant bibliographies by topics / ANFIS like-PID

Contents

Journal articles

Academic literature on the topic 'ANFIS like-PID'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'ANFIS like-PID.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "ANFIS like-PID"

1

Berbek, Mohammed I., and Ahmed A. Oglah. "Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management in internet congestion." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 229–42. https://doi.org/10.11591/ijeecs.v26.i1.pp229-242.

Full text

Abstract:

Routers are vital during network congestion. All routers have input and output packet buffers. Various congestion control strategies have been suggested. Some controller-based proportional-integral derivative (PIDs) have recently been offered as active queue management (AQM) solutions to alleviate the deterioration of transmission control protocol (TCP) congestion management system performance. However, the time delay is large, the data retention decreases, and oscillation occurs, suggesting that the present PIDcontroller is unable to fulfill quality of service (QoS) criteria. Some research is

APA, Harvard, Vancouver, ISO, and other styles

2

I. Berbek, Mohammed, and Ahmed A. Oglah. "Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management in internet congestion." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (2022): 229. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp229-242.

Full text

Abstract:

Routers are vital during network congestion. All routers have input and output packet buffers. V<span lang="EN-US">Various congestion control strategies have been suggested. Some controller-based proportional-integral derivative (PIDs) have recently been offered as active queue management (AQM) solutions to alleviate the deterioration of transmission control protocol (TCP) congestion management system performance. However, the time delay is large, the data retention decreases, and oscillation occurs, suggesting that the present PID-controller is unable to fulfill quality of service (QoS)

APA, Harvard, Vancouver, ISO, and other styles

3

.., Nirmal Kumar, Manish Prateek, Neeta Singh, and Abhinav Saxena. "An Implicit Controlling of Adaptive Neuro Fuzzy Inference System Controller for The Grid Connected Wind Driven PMSG System." Fusion: Practice and Applications 12, no. 2 (2023): 193–205. http://dx.doi.org/10.54216/fpa.120216.

Full text

Abstract:

The article presents the design and control of the adaptive neuro fuzzy Inference system (ANFIS) for the wind-driven permanent magnet synchronous generator (PMSG) in the grid connected system. The rectifier and inverter are connected with the PMSG output and the grid for maintaining the voltage at the grid under variable wind operations. Such interconnections have many challenges, like high harmonics at the output and an improper voltage profile. The harmonics are measured in terms of total harmonic distortion (THD). Performance parameters like peak overshoot and settling time of DC link volta

APA, Harvard, Vancouver, ISO, and other styles

4

Lutfy, O. F., Mohd S. B. Noor, M. H. Marhaban, and K. A. Abbas. "A genetically trained adaptive neuro-fuzzy inference system network utilized as a proportional-integral-derivative-like feedback controller for non-linear systems." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 223, no. 3 (2008): 309–21. http://dx.doi.org/10.1243/09596518jsce683.

Full text

Abstract:

This paper presents a genetically trained PID (proportional-integral-derivative)-like ANFIS (adaptive neuro-fuzzy inference system) acting as a feedback controller to control non-linear systems. Three important issues are addressed in this paper, which are, first, the evaluation of the ANFIS as a PID-like controller; second, the utilization of the GA (genetic algorithm) alone to train the ANFIS controller, instead of the hybrid learning methods that are widely used in the literature; and, third, the determination of the input and output scaling factors for this controller by the GA. The GA, wi

APA, Harvard, Vancouver, ISO, and other styles

5

Reddy, K. M. N. Chaitanya kumar, and Dr N. Kanagasabai. "Performance Analysis of ANFIS-PID Controller based Speed Regulation and Harmonic Reduction in BLDC Motor Application." International Journal of Electrical and Electronics Research 12, no. 1 (2024): 187–94. http://dx.doi.org/10.37391/ijeer.120127.

Full text

Abstract:

This study focuses on assessing the performance of a Proportional-Integral-Derivative (PID) controller integrated with an Adaptive Neuro-Fuzzy Inference System (ANFIS) in the context of speed regulation and harmonic reduction in Brushless DC (BLDC) motor applications. Rising BLDC motor speed elevates Total harmonic distortion (THD) due to non-linearity. THD reduction is vital for efficiency, reliability, and compliance in applications like electric vehicles, HVAC, and industrial automation, ensuring optimal performance and longevity. Through simulation-based design and implementation, the effe

APA, Harvard, Vancouver, ISO, and other styles

6

Sharma, Deepesh. "Automatic generation control of multi source interconnected power system using adaptive neuro-fuzzy inference system." International Journal of Engineering, Science and Technology 12, no. 3 (2020): 66–80. http://dx.doi.org/10.4314/ijest.v12i3.7.

Full text

Abstract:

LFC (Load Frequency Control) difficulty is created by load of power system variations. Extreme acceptable frequency distinction is ±0.5 Hz which is extremely intolerable. Here, LFC is observed by PID controller (PID-C), Fuzzy and ANFIS controller (ANFIS-C). To control different errors like frequency and area control error (ACE) in spite of occurrences of load disturbance and uncertainties of system is checked by MATLAB/SIMULINK software. Proposed Controller offers less, and small peak undershoot, speedy response to make final steady state. LFC is mandatory for reliability of large interconnect

APA, Harvard, Vancouver, ISO, and other styles

7

Parmjit Singh, Prince Jindal and Simerpreet Singh. "An Improved Hybrid Fuzzy-PID Tunning With Particle Sawrm Optimization For Enhancing Induction Motor Performance." International Journal for Modern Trends in Science and Technology 7, no. 07 (2022): 66–71. http://dx.doi.org/10.46501/ijmtst051234.

Full text

Abstract:

The fuzzy logic controllers are estimated as an appropriate controller because it is minimally complex method and did not involve any of the mathematical models. The major concern of this study is to control the fluctuations in speed of the induction motor through improving the conventional mechanism by utilizing the ANFIS paradigm as controller. Therefore a new mechanism is to be projected that will execute ANFIS. Because of the merits like Adaptive learning, Self-Organization, Real Time Operation, Fault Tolerance through Redundant Information Coding etc. The ANFIS algorithm is utilized as a

APA, Harvard, Vancouver, ISO, and other styles

8

Abdulla, Shwan. "Comparative Assessment of PID, Fuzzy Logic and ANFIS Controllers in an Automatic Voltage Regulator of A Power System." Jordan Journal of Electrical Engineering 8, no. 4 (2022): 379. http://dx.doi.org/10.5455/jjee.204-1664025424.

Full text

Abstract:

A comparative study and performance analysis of three different controllers - namely proportional-integral-derivative (PID), PD-like fuzzy logic and adaptive neuro fuzzy inference system (ANFIS) - utilized to control the output voltage of an automatic voltage regulator (AVR) of a power system are carried out. The obtained results show that the PID controller is capable of rejecting simultaneous disturbance signals effectively with zero steady-state error (SSE). However, it is not robust to unexpected parameter changes of the system. On the other hand, the fuzzy logic controller shows the abili

APA, Harvard, Vancouver, ISO, and other styles

9

M. Venkatesh and G. Raja Rao. "Speed Control of DC Motor Using Intelligent Controllers." November 2020 6, no. 11 (2020): 157–64. http://dx.doi.org/10.46501/ijmtst061130.

Full text

Abstract:

DC Motors are broadly utilized in mechanical applications, home appliances and robot controllers on account of their high unwavering quality, adaptability and low cost, where speed and position control of motor are required. The activity of a DC motor is performed by conventional controllers and intelligent controllers in MATLAB environment. The speed control of a dc motor utilizing conventional controllers (PID, IMC) and intelligent controllers (FLC, ANFIS) in view of MATLAB simulation program. A numerical model of the process has been created utilizing genuine plant information and afterward

APA, Harvard, Vancouver, ISO, and other styles

10

Kumar Saliganti. "Implementation of ANFIS-Based PID Controller in a Three-Phase Solar PV-Integrated UPQC for Power Quality Improvement." Journal of Information Systems Engineering and Management 10, no. 29s (2025): 229–46. https://doi.org/10.52783/jisem.v10i29s.4472.

Full text

Abstract:

The incorporation of renewable energy sources into the grid presents challenges like reactive power compensation, harmonics, voltage sags, and swells, which impact power quality. This paper proposes a single-stage PV-linked Unified Power Quality Conditioner (PV-UPQC) for a three-phase grid-connected system to address these issues while optimizing PV energy utilization. The system employs Perturb and Observe (P&O) MPPT for maximum power extraction and an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based PID controller for dynamic inverter switching, ensuring precise tuning and adaptabilit

APA, Harvard, Vancouver, ISO, and other styles

More sources

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!