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PID control has been the dominant control strategy in the process industry due to its simplicity in design and effectiveness in controlling a wide range of processes.
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Applying neural networks to on-line updated PID controllers for nonlinear process control
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PID tuning rules for SOPDT systems: Review and some new results
Panda RC, Yu CC, Huang HP · 2004
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Optimal design of PID parameters by evolution algorithm
Zhou K, Zhen L · 2005
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Optimization of a fuzzy PI controller using reinforcement learning
Boubertakh H, Glorennec PY · 2006
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A stable self-learning PID control for multivariable time varying systems
Yu D, Chang T, Yu D · 2007
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A proposal of adaptive PID controller based on reinforcement learning
Wang XS, Cheng YH, Wei S · 2007
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Adaptive PID controller based on reinforcement learning for wind turbine control
Sedighizadeh M, Rezazadeh A · 2008
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Tuning fuzzy PD and PI controllers using reinforcement learning
Boubertakh H, Tadjine M, Glorennec PY, Labiod S · 2010
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Off-policy actor-critic
Degris T, White M, Sutton RS · 2012
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Four types of controllers
Ellis G · 2012
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Improve PID controller through reinforcement learning
Qin Y, Zhang W, Shi J, Liu J · 2018
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Toward human-in-the-loop PID control based on CACLA reinforcement learning
Zhong J, Li Y · 2019
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Reinforcement Learning–Overview of recent progress and implications for process control
Shin J, Badgwell TA, Liu KH, Lee JH · 2019
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Multicopter PID attitude controller gain auto-tuning through reinforcement learning neural networks
Park D, Yu H, Xuan-Mung N, Lee J, Hong SK · 2019
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Value constrained model-free continuous control
Bohez S, Abdolmaleki A, Neunert M, Buchli J, Heess N, et al · 2019
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A review of PID control, tuning methods and applications
Borase RP, Maghade D, Sondkar S, Pawar S · 2020
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Application of reinforcement learning on self-tuning PID controller for soccer robot multi-agent system
El Hakim A, Hindersah H, Rijanto E · 2013
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Deterministic policy gradient algorithms
Silver D, Lever G, Heess N, Degris T, Wierstra D, et al · 2014
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Continuous control with deep reinforcement learning
Lillicrap TP, Hunt JJ, Pritzel A, Heess N, Erez T, et al · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe S, Szegedy C · 2015
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Process dynamics and control
Seborg DE, Edgar TF, Mellichamp DA, Doyle III FJ · 2016
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An overview of dynamic-linearization-based data-driven control and applications
Hou Z, Chi R, Gao H · 2016
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Reinforcement learning-based adaptive PID controller for DPS
Lee D, Lee SJ, Yim SC · 2020
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PID controller gains tuning using metaheuristic optimization methods: A survey
Abushawish A, Hamadeh M, Nassif AB · 2020
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Reinforcement learning based design of linear fixed structure controllers
Lawrence NP, Stewart GE, Loewen PD, Forbes MG, Backstrom JU, et al · 2020
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Optimal PID and antiwindup control design as a reinforcement learning problem
Lawrence NP, Stewart GE, Loewen PD, Forbes MG, Backstrom JU, et al · 2020
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Deep reinforcement learning for process control: A primer for beginners
Spielberg S, Tulsyan A, Lawrence NP, Loewen PD, Gopaluni RB · 2020
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Design of a reinforcement learning PID controller
Guan Z, Yamamoto T · 2021
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A novel sample-efficient deep reinforcement learning with episodic policy transfer for PID-based control in cardiac catheterization robots
Omisore OM, Akinyemi T, Duan W, Du W, Wang L · 2021
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Policy learning with constraints in model-free reinforcement learning: A survey
Liu Y, Halev A, Liu X · 2021
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A pragmatic approach to robust gain scheduling
Stewart GE · 2025
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