Implementations of TD3 and DDPG Reinforcement Learning Techniques for Tuning PID Controller of TRMS System

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dc.contributor.authorTufenkci, Sevilay
dc.contributor.authorAlagoz, Baris Baykant
dc.contributor.authorKavuran, Gurkan
dc.contributor.authorYeroglu, Celaleddin
dc.contributor.authorHerencsar, Norbert
dc.contributor.authorMahata, Shibendu
dc.date.accessioned2026-04-04T13:37:26Z
dc.date.available2026-04-04T13:37:26Z
dc.date.issued2025
dc.departmentİnönü Üniversitesi
dc.description.abstractReinforcement Learning (RL) is a learning method that utilizes interactions between agents and their environments, providing a valuable tool for controller design through simulations. However, traditional industrial systems such as PID control loops have yet to fully embrace the advantages of RL algorithms for effectively tuning controllers. This study presents an experimental initiative demonstrating the implementation of an RL-driven method for optimal PID controller tuning to address challenges in rotor control, explicitly focusing on the Twin-Rotor Multi-Input Multi-Output System (TRMS). Rotor control presents a complex challenge involving aerodynamics and external disturbances. The research implements two RL algorithms, namely the Deep Deterministic Policy Gradient (DDPG) and the Twin Delay Deep Deterministic Policy Gradient (TD3), in a tailored simulation environment to train RL agents to achieve optimal PID control dynamics. Results of simulation and experimental studies indicate that RL algorithms can be implemented for PID controller tuning when the simulation environment for training the RL algorithms well-represent the dominating dynamics and control complications of real-world systems. In this case, both the simulation and experimental results are in good-agreement.
dc.identifier.doi10.1007/s11518-025-5693-5
dc.identifier.issn1004-3756
dc.identifier.issn1861-9576
dc.identifier.orcid0000-0001-9815-7724
dc.identifier.scopus2-s2.0-105014165564
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://doi.org/10.1007/s11518-025-5693-5
dc.identifier.urihttps://hdl.handle.net/11616/109812
dc.identifier.wosWOS:001556800000001
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Heidelberg
dc.relation.ispartofJournal of Systems Science and Systems Engineering
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250329
dc.subjectDeep reinforcement learning
dc.subjectDirect Current (DC) motor
dc.subjectPID controller
dc.subjectDeep Deterministic Policy Gradient (DDPG)
dc.subjecttwin-rotor multi-input multi-output system
dc.titleImplementations of TD3 and DDPG Reinforcement Learning Techniques for Tuning PID Controller of TRMS System
dc.typeArticle

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