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Adaptive tuning of fractional order PID controllers for nonlinear processes using hybrid PSO DQN reinforcement learning

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Title: Adaptive tuning of fractional order PID controllers for nonlinear processes using hybrid PSO DQN reinforcement learning
Authors: Shahouni, Reza, Bahraini, Masoud, 1991, Abrofarakh, Moslem, Abbasi, Mohsen
Source: Scientific Reports. 15(1)
Subject Terms: PID controller tuning, GA, Deep Q-Network, ANN, Non-Linear FOPID, PSO-RL
Description: This study presents an innovative adaptive non-linear fractional-order PID (FOPID) tuning methodology for a flow meter controller in a desalination plant, integrating a hybrid Particle Swarm Optimization (PSO) and Deep Q-Network (DQN)-based Reinforcement Learning (RL) strategy with a dynamic weighting mechanism to optimize control of non-linear systems with time delays and disturbances. By utilizing fractional-order parameters, the PSO-DQN-RL framework ensures global optimization and real-time adaptability under fluctuations in operational parameters. Results demonstrate superior performance over traditional methods and advanced techniques such as Genetic Algorithms (GA), Fuzzy Logic Controller (FLC), Neural Network-based PID (NN-PID), and PSO, offering faster response times, reduced overshoot, and minimal steady-state error compared to the slower and less precise outcomes of FLC, the static limitations of PSO, the rigid parameter settings of GA, and the inconsistent performance of NN. The hybrid method's enhanced robustness and dynamic parameter evolution surpass the modest adaptability of PSO. Despite its computational complexity, the offline-online balance and real-time GUI enable scalable deployment, positioning this scientifically novel approach as a benchmark for FOPID tuning in various applications.
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Adaptive tuning of fractional order PID controllers for nonlinear processes using hybrid PSO DQN reinforcement learning
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Shahouni%2C+Reza%22">Shahouni, Reza</searchLink><br /><searchLink fieldCode="AR" term="%22Bahraini%2C+Masoud%22">Bahraini, Masoud</searchLink>, 1991<br /><searchLink fieldCode="AR" term="%22Abrofarakh%2C+Moslem%22">Abrofarakh, Moslem</searchLink><br /><searchLink fieldCode="AR" term="%22Abbasi%2C+Mohsen%22">Abbasi, Mohsen</searchLink>
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  Data: <i>Scientific Reports</i>. 15(1)
– Name: Subject
  Label: Subject Terms
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  Data: <searchLink fieldCode="DE" term="%22PID+controller+tuning%22">PID controller tuning</searchLink><br /><searchLink fieldCode="DE" term="%22GA%22">GA</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+Q-Network%22">Deep Q-Network</searchLink><br /><searchLink fieldCode="DE" term="%22ANN%22">ANN</searchLink><br /><searchLink fieldCode="DE" term="%22Non-Linear+FOPID%22">Non-Linear FOPID</searchLink><br /><searchLink fieldCode="DE" term="%22PSO-RL%22">PSO-RL</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: This study presents an innovative adaptive non-linear fractional-order PID (FOPID) tuning methodology for a flow meter controller in a desalination plant, integrating a hybrid Particle Swarm Optimization (PSO) and Deep Q-Network (DQN)-based Reinforcement Learning (RL) strategy with a dynamic weighting mechanism to optimize control of non-linear systems with time delays and disturbances. By utilizing fractional-order parameters, the PSO-DQN-RL framework ensures global optimization and real-time adaptability under fluctuations in operational parameters. Results demonstrate superior performance over traditional methods and advanced techniques such as Genetic Algorithms (GA), Fuzzy Logic Controller (FLC), Neural Network-based PID (NN-PID), and PSO, offering faster response times, reduced overshoot, and minimal steady-state error compared to the slower and less precise outcomes of FLC, the static limitations of PSO, the rigid parameter settings of GA, and the inconsistent performance of NN. The hybrid method's enhanced robustness and dynamic parameter evolution surpass the modest adaptability of PSO. Despite its computational complexity, the offline-online balance and real-time GUI enable scalable deployment, positioning this scientifically novel approach as a benchmark for FOPID tuning in various applications.
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        Value: 10.1038/s41598-025-22509-x
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      – Text: English
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      – SubjectFull: GA
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      – SubjectFull: Deep Q-Network
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            NameFull: Shahouni, Reza
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              M: 01
              Type: published
              Y: 2025
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