Neural Network Based Self-Tuning PID Controller

Xu, Pengyi

doi:10.1109/ahpcai57455.2022.10087411

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2024

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Cited by 3 publications

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NC-SIMC: Neuro-Controller Simple Internal Model Control

Costa,

Skogestad,

dos Reis Nogueira

2024

IFAC-PapersOnLine

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NC-SIMC: Neuro-Controller Simple Internal Model Control

Costa,

Skogestad,

dos Reis Nogueira

2024

IFAC-PapersOnLine

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Adaptive PID Controller Using Neural Network for Pressure Drop in Nonlinear Fluid Systems

Bawazir,

AL-Yazidi,

Al-Dogail

et al. 2024

2024 21st International Multi-Conference on Systems, Signals &Amp;amp; Devices (SSD)

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A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks

Marrero,

Kern,

Urrea

2024

Sensors

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This paper investigates spiking neural networks (SNN) for novel robotic controllers with the aim of improving accuracy in trajectory tracking. By emulating the operation of the human brain through the incorporation of temporal coding mechanisms, SNN offer greater adaptability and efficiency in information processing, providing significant advantages in the representation of temporal information in robotic arm control compared to conventional neural networks. Exploring specific implementations of SNN in robot control, this study analyzes neuron models and learning mechanisms inherent to SNN. Based on the principles of the Neural Engineering Framework (NEF), a novel spiking PID controller is designed and simulated for a 3-DoF robotic arm using Nengo and MATLAB R2022b. The controller demonstrated good accuracy and efficiency in following designated trajectories, showing minimal deviations, overshoots, or oscillations. A thorough quantitative assessment, utilizing performance metrics like root mean square error (RMSE) and the integral of the absolute value of the time-weighted error (ITAE), provides additional validation for the efficacy of the SNN-based controller. Competitive performance was observed, surpassing a fuzzy controller by 5% in terms of the ITAE index and a conventional PID controller by 6% in the ITAE index and 30% in RMSE performance. This work highlights the utility of NEF and SNN in developing effective robotic controllers, laying the groundwork for future research focused on SNN adaptability in dynamic environments and advanced robotic applications.

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Neural Network Based Self-Tuning PID Controller

Cited by 3 publications

References 9 publications

NC-SIMC: Neuro-Controller Simple Internal Model Control

NC-SIMC: Neuro-Controller Simple Internal Model Control

Adaptive PID Controller Using Neural Network for Pressure Drop in Nonlinear Fluid Systems

A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks

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