Self-tuning neural network PID with dynamic response control

O Rodríguez-Abreo, J Rodríguez-Reséndiz… - IEEE …, 2021 - ieeexplore.ieee.org
PID controllers are widely used and adaptable to various types of systems. However, for the
response to be adequate under different conditions, the PID gains must be adjusted. The …

A fuzzy-PID series feedback self-tuned adaptive control of reactor power using nonlinear multipoint kinetic model under reference tracking and disturbance rejection

A Aftab, X Luan - Annals of Nuclear Energy, 2022 - Elsevier
In this work, a series feedback combination of fuzzy and PID controllers is designed to
resolve the reactor power control problem for VVER-1000 PWR. A nonlinear multipoint …

Neural fractional order PID controllers design for 2-link rigid robot manipulator

MJ Mohamed, BK Oleiwi, LH Abood, AT Azar… - Fractal and …, 2023 - mdpi.com
The robotic manipulator is considered one of the complex systems that include multi-input,
multi-output, non-linearity, and highly coupled. The uncertainty in the parameters and …

A pid control algorithm with adaptive tuning using continuous artificial hydrocarbon networks for a two-tank system

J Sánchez-Palma, JL Ordoñez-Ávila - IEEE Access, 2022 - ieeexplore.ieee.org
Owing to their ease of implementation, proportional-integral-derivative (PID) control systems
are widely used to control physical systems. However, when environmental disturbances or …

Real-time drift-driving control for an autonomous vehicle: Learning from nonlinear model predictive control via a deep neural network

T Lee, D Seo, J Lee, Y Kang - Electronics, 2022 - mdpi.com
A drift-driving maneuver is a control technique used by an expert driver to control a vehicle
along a sharply curved path or slippery road. This study develops a nonlinear model …

Self-Tuning Control Using an Online-Trained Neural Network to Position a Linear Actuator

R Hernandez-Alvarado, O Rodriguez-Abreo… - Micromachines, 2022 - mdpi.com
Linear actuators are widely used in all kinds of industrial applications due to being devices
that convert the rotation motion of motors into linear or straight traction/thrust motion. These …

Performance analysis of deep neural network controller for autonomous driving learning from a nonlinear model predictive control method

T Lee, Y Kang - Electronics, 2021 - mdpi.com
Nonlinear model predictive control (NMPC) is based on a numerical optimization method
considering the target system dynamics as constraints. This optimization process requires …

Enhanced precision in robot arm positioning: A nonlinear damping approach for flexible joint manipulators

AH Jafari, R Dhaouadi, R Jafari - IET Control Theory & …, 2024 - Wiley Online Library
This article introduces an advanced nonlinear controller designed for optimizing the
performance of a single‐link robot arm featuring a flexible joint. The proposed nonlinear …

Design of Hydropower Plant PID Controller Parameters Using Artificial Neural Networks

R Koleva, D Babunski, E Zaev - 2024 International Conference …, 2024 - ieeexplore.ieee.org
This paper represents the artificial neural networks indirect implementation in non-linear
control systems. Due to the popularity of the proportional, integral, and derivative, PID …

DA Motor Kontrolünde Veri Güdümlü ve Model Tabanlı Yöntemlerin Ani Yük Değişimlerine Karşı Tepkilerinin Analizi

G Sonugür - Politeknik Dergisi, 2023 - dergipark.org.tr
Doğru Akım (DA) motor hız denetleyicilerinde bozucu etkilere karşı direnç gösterme ve her
türlü dış etki karşısında referans noktasını en az hata ile takip etmek kritik öneme sahiptir. DA …