Chaotic Self-Tuning PID Controller Based on Fuzzy Wavelet Neural Network Model

Davanipour, Mehrnoush; Javanmardi, Hamidreza; Goodarzi, N.

doi:10.1007/s40998-018-0069-1

Cited by 23 publications

(6 citation statements)

References 23 publications

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“…According to the above discussions, this study presents a method for auto-tuning PID parameters using multilayer fuzzy neural network (PID-MFNN) to control the attitude the quadcopters. Compared with the previous studies (Wang et al, 2017 ; Davanipour et al, 2018 ; Tripathy et al, 2019 ), our proposed PID-MFNN has some advantages, such as no need for offline training. The major differences and improvements among this study and related works are shown in Table 1 .…”

Section: Introductionmentioning

confidence: 84%

“…In which, using the fuzzy neural network to tune PID parameter controllers has attracted the attention of many researchers. In 2018, Davanipour et al proposed a self-tuning PID controller based on a fuzzy wavelet neural network (Davanipour et al, 2018 ). In 2019, Tripathy et al introduced a fuzzy PID controller for load frequency control using spider monkey optimization (Tripathy et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Online Tuning of PID Controller Using a Multilayer Fuzzy Neural Network Design for Quadcopter Attitude Tracking Control

Park

Quynh

et al. 2021

Front. Neurorobot.

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This study presents an online tuning proportional-integral-derivative (PID) controller using a multilayer fuzzy neural network design for quadcopter attitude control. PID controllers are simple but effective control methods. However, finding the suitable gain of a model-based controller is relatively complicated and time-consuming because it depends on external disturbances and the dynamic modeling of plants. Therefore, the development of a method for online tuning of quadcopter PID parameters may save time and effort, and better control performance can be achieved. In our controller design, a multilayer structure was provided to improve the learning ability and flexibility of a fuzzy neural network. Adaptation laws to update network parameters online were derived using the gradient descent method. Also, a Lyapunov analysis was provided to guarantee system stability. Finally, simulations concerning quadcopter attitude control were performed using a Gazebo robotics simulator in addition to a robot operating system (ROS), and their results were demonstrated.

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Section: Introductionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Online Tuning of PID Controller Using a Multilayer Fuzzy Neural Network Design for Quadcopter Attitude Tracking Control

Park

Quynh

et al. 2021

Front. Neurorobot.

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“…Pour modéliser l'écoulement multiphasique comme l'étude de la surface, nous choisissons de réaliser la reconstruction de l'interface eau-air par la méthode VOF [11] sous Ansys Fluent. Dans cette méthode, chaque cellule de calcul est supposée contenir éventuellement un mélange des deux fluides.…”

Section: I2 Méthode Du Volume De Fluide (Vof)unclassified

Simulation Numérique De La Propagation Des Vagues Franchissant Un Obstacle Et Modélisation Des Résultats Obtenus Par Réseau De Neurones Et Neuro-Flou

Michael

Philémon

Soanandrianina

et al. 2022

IJPSAT

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Ce travail a pour objectif d’analyser et d’évaluer l’effet de la bathymétrie sur la surface de la mer par la méthode numérique et de modéliser les données de simulations par réseau de neurone et neuro-flou. Les simulations numériques sont effectuées à l’aide du logiciel Ansys Fluent qui utilise la méthode de Volume of Fluid et le modèle de turbulence ; les données sont traitées sur Matlab. Nous avons déterminé sept simulations (M0, M1, M2, M3, M4, M5 et M6). Les résultats ont montré que la morphologie (hauteur maximale) et le comportement (position de la hauteur maximale et position de déferlement) des vagues varient en fonction de la hauteur de l’obstacle. Les modèles par intelligences artificielles, sont excellents avec des valeurs de l’ erreur quadratique moyenne faibles et les critères de Nash sont supérieurs à 90%.

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“…The criterion for the occurrence of capillary snap-off in a pore-throat is determined by the pore-throat geometry and fluid properties, the interfacial tension σ and wettability θ. First, we revisit the derivation of the snap-off criterion for a pore-throat channel with circular cross-sections, as shown in Figure 1a), which has been investigated by many researchers (Hirt & Nichols, 1981). Then, we derive the criterion for a pore-throat channel with a rectangular cross-section (as shown in Figure 1b) following the same procedure.…”

Section: Theoretical Analysismentioning

confidence: 99%

Geometric Criteria for the Snap‐Off of a Non‐Wetting Droplet in Pore‐Throat Channels With Rectangular Cross‐Sections

Cha

Xie

Feng

et al. 2021

Water Resources Research

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The displacement of a nonowetting fluid by a wetting fluid in a pore-throat (expansion-contraction-expansion) channel may result in the breakup of the non-wetting fluid into separate droplets/bubbles. This phenomenon, referred to as snap-off, choke-off, or breakup (Herring et al., 2018;Xu, Liang, et al., 2017;, occurs when the capillary pressure at its leading part exceeds that at the pore-throat junction during the imbibition process. It is an important process in many subsurface engineering applications, such as aquifer remediation (

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Chaotic Self-Tuning PID Controller Based on Fuzzy Wavelet Neural Network Model

Cited by 23 publications

References 23 publications

Online Tuning of PID Controller Using a Multilayer Fuzzy Neural Network Design for Quadcopter Attitude Tracking Control

Online Tuning of PID Controller Using a Multilayer Fuzzy Neural Network Design for Quadcopter Attitude Tracking Control

Simulation Numérique De La Propagation Des Vagues Franchissant Un Obstacle Et Modélisation Des Résultats Obtenus Par Réseau De Neurones Et Neuro-Flou

Geometric Criteria for the Snap‐Off of a Non‐Wetting Droplet in Pore‐Throat Channels With Rectangular Cross‐Sections

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