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This work proposes a control-informed reinforcement learning (CIRL) framework that integrates proportional-integral-derivative (PID) control components into the architecture of deep reinforcement learning (RL) policies.
“CAQL: Continuous Action Q-Learning”, 2020
Moonkyung Ryu et al · 1909
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K.J. Åström and T. Hägglund · 1984
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“Internal model control: PID controller design”
Daniel. Rivera, Manfred Morari and Sigurd Skogestad · 1986
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Ronald Williams · 1992
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“Neuro-fuzzy modeling and control of a batch process involving simultaneous reaction and distillation”
JA Wilson and EC Martinez · 1997
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“Model predictive control: past, present and future”
Manfred Morari and Jay H. Lee · 1999
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“Policy gradient methods for reinforcement learning with function approximation”
Richard Sutton, David McAllester, Satinder Singh and Yishay Mansour · 1999
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“Nonlinear model predictive control using neural networks”
Stephen Piche, Bijan Sayyar-Rodsari, Doug Johnson and Mark Gerules · 2000
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“Increasing Customer Value of Industrial Control Performance Monitoring—Honeywell’s Experience”, 2002
Lane Desborough and Randy Miller · 2002
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“Virtual reference feedback tuning: a direct method for the design of feedback controllers”
M.C. Campi, A. Lecchini and S.M. Savaresi · 2002
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Niket Kaisare, Jong Lee and Jay Lee · 2003
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“Simple analytic rules for model reduction and PID controller tuning”
Sigurd Skogestad · 2003
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“Gaussian process model based predictive control”
Jus Kocijan, Roderick Murray-Smith, Carl Rasmussen and Agathe Girard · 2004
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“Optimal control of a fed-batch bioreactor using simulation-based approximate dynamic programming”
Catalina Peroni, Niket Kaisare and Jay Lee · 2005
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Jay Lee and Jong Lee · 2006
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“Adaptive PID controller based on reinforcement learning for wind turbine control”
Mostafa Sedighizadeh and Alireza Rezazadeh · 2008
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“Dynamic tuning of PI-controllers based on model-free reinforcement learning methods”
Lena Brujeni, Jong Lee and Sirish Shah · 2010
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“Neurodynamic programming approach for the PID controller adaptation”
Marcus Berger and JoÃo da Fonseca · 2013
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“Playing Atari with Deep Reinforcement Learning”, 2013
Volodymyr Mnih et al · 2013
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Michael Forbes, Rohit Patwardhan, Hamza Hamadah and R Gopaluni · 2015
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Max Mowbray, Robin Smith, Ehecatl Del-Chanona and Dongda Zhang · 2021
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“A novel implicit hybrid machine learning model and its application for reinforcement learning”
Derek Machalek, Titus Quah and Kody. Powell · 2021
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“DiffLoop: Tuning PID controllers by differentiating through the feedback loop”
Athindran Kumar and Peter Ramadge · 2021
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“Stability-preserving automatic tuning of PID control with reinforcement learning”
Ayub Lakhani, Myisha Chowdhury and Qiugang Lu · 2021
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“A review of safe reinforcement learning: Methods, theory and applications”
Shangding Gu et al · 2022
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“Proximal Policy Optimization Algorithms”, 2017
John Schulman et al · 2017
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“Model-based 2-D look-up table calibration tool development”
Ertugrul Ondes, Ismail Bayezit, Imre Poergye and Ahmed Hafsi · 2017
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“Reinforcement learning–overview of recent progress and implications for process control”
Thomas Badgwell, Jay Lee and Kuang-Hung Liu · 2018
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“Distributed adaptive dynamic programming for data-driven optimal control”
Wentao Tang and Prodromos Daoutidis · 2018
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“Reinforcement Learning: An Introduction”
Richard. Sutton and Andrew. Barto · 2018
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“Addressing Function Approximation Error in Actor-Critic Methods”, 2018
Scott Fujimoto, Herke van Hoof and David Meger · 2018
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Panagiotis Petsagkourakis et al · 2022
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In Journal of Process Control 115
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“Automated deep reinforcement learning for real-time scheduling strategy of multi-energy system integrated with post-carbon and direct-air carbon captured system”
Tobi Alabi et al · 2022
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“Deep reinforcement learning with shallow controllers: An experimental application to PID tuning”
Nathan Lawrence et al · 2022
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“Meta-reinforcement learning for the tuning of PI controllers: An offline approach”
Daniel. McClement et al · 2022
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“Distributional reinforcement learning for inventory management in multi-echelon supply chains”
Guoquan Wu, MiguelÁngel de Carvalho Servia and Max Mowbray · 2022
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“Noniterative Data-Driven Gain-Scheduled Controller Design Based on Fictitious Reference Signal”
Shuichi Yahagi and Itsuro Kajiwara · 2023
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Marwan Mousa et al · 2023
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“A data-driven tracking control framework using physics-informed neural networks and deep reinforcement learning for dynamical systems”
R.R. Faria, B.D.O. Capron, A.R. Secchi and M.B. De Souza · 2023
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“A tutorial on derivative-free policy learning methods for interpretable controller representations”
Joel Paulson, Farshud Sorourifar and Ali Mesbah · 2023
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