참고문헌 (80)

1. Journal of Process Control Å kesson 16 937 2006 10.1016/j.jprocont.2006.06.001 A neural network model predictive controller 

   상세보기

2. Achiam 22 2017 International conference on machine learning Constrained policy optimization 

3. International Journal of Adaptive Control and Signal Processing Adetola 25 155 2011 10.1002/acs.1193 Robust adaptive MPC for constrained uncertain nonlinear systems 

   상세보기

4. Alessio 345 2009 Nonlinear model predictive control A survey on explicit model predictive control 

5. Journal-Chinese Institute of Chemical Engineers Allgower 35 299 2004 Nonlinear model predictive control: From theory to application 

   상세보기

6. Chemical Engineering Journal Altınten 138 490 2008 10.1016/j.cej.2007.07.029 Self-tuning PID control of jacketed batch polystyrene reactor using genetic algorithm 

   상세보기

7. AIChE Journal Aumi 58 2105 2012 10.1002/aic.12720 Integrating data-based modeling and nonlinear control tools for batch process control 

   상세보기

8. Automatica Bemporad 38 3 2002 10.1016/S0005-1098(01)00174-1 The explicit linear quadratic regulator for constrained systems 

   상세보기

9. IFAC-PapersOnLine Benosman 49 253 2016 10.1016/j.ifacol.2016.07.960 BayesIan optimization-based modular indirect adaptive control for a class of nonlinear systems 

   상세보기

10. Chemical Engineering Research & Design Berber 74 3 1996 Control of batch reactors-a review (reprinted from methods of model based process control, 1995) 

11. Bishop 2006 Pattern recognition and machine learning 

12. IEEE Control Systems Magazine Bonvin 26 34 2006 10.1109/MCS.2006.252831 Control and optimization of batch processes 

    상세보기

13. Byun 3016 2020 2020 59th IEEE conference on decision and control Robust dual control of batch processes with parametric uncertainty using proximal policy optimization 

14. Campbell 2150 2002 Proceedings of the 2002 American control conference, vol. 3 A comparison of run-to-run control algorithms 

15. Neural Computing & Applications Cavagnari 8 86 1999 10.1007/s005210050010 Neural network implementation of nonlinear receding-horizon control 

    상세보기

16. Journal of Process Control Chen 15 665 2005 10.1016/j.jprocont.2005.01.001 Particle filters for state and parameter estimation in batch processes 

    상세보기

17. Chen 1520 2018 2018 annual american control conference Approximating explicit model predictive control using constrained neural networks 

18. Chen 2019 Large scale model predictive control with neural networks and primal active sets 

19. Chowdhary 29 2013 Advances in aerospace guidance, navigation and control Concurrent learning adaptive model predictive control 

20. Computers & Chemical Engineering Cuthrell 13 49 1989 10.1016/0098-1354(89)89006-4 Simultaneous optimization and solution methods for batch reactor control profiles 

    상세보기

21. Duff 2002 Optimal learning: computational procedures for bayes-adaptive markov decision processes 

22. Journal of Process Control Ellis 24 1156 2014 10.1016/j.jprocont.2014.03.010 A tutorial review of economic model predictive control methods 

    상세보기

23. Automation and Remote Control Feldbaum 21 874 1960 Dual control theory 

24. IEE Proceedings D (Control Theory and Applications) Filatov 147 118 2000 10.1049/ip-cta:20000107 Survey of adaptive dual control methods 

    상세보기

25. Journal of Machine Learning Research Garcıa 16 1437 2015 A comprehensive survey on safe reinforcement learning 

    상세보기

26. Ghavamzadeh 2016 BayesIan reinforcement learning: A survey 

27. Haarnoja 1861 2018 International conference on machine learning Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor 

28. IFAC-PapersOnLine Jang 49 37 2016 10.1016/j.ifacol.2016.07.213 A robust NMPC scheme for semi-batch polymerization reactors 

    상세보기

29. AIChE Journal Jiang 2021 10.1002/aic.17282 Convolutional neural nets in chemical engineering: Foundations, computations, and applications 

    상세보기

30. Automatica Johansen 40 293 2004 10.1016/j.automatica.2003.09.021 Approximate explicit receding horizon control of constrained nonlinear systems 

    상세보기

31. Science Jordan 349 255 2015 10.1126/science.aaa8415 Machine learning: Trends, perspectives, and prospects 

    상세보기

32. IEEE Transactions on Cybernetics Karg 50 3866 2020 10.1109/TCYB.2020.2999556 Efficient representation and approximation of model predictive control laws via deep learning 

    상세보기

33. Industrial and Engineering Chemistry Research Kim 58 13664 2019 10.1021/acs.iecr.8b06233 Robust batch-to-batch optimization with scenario adaptation 

    상세보기

34. Journal of Process Control Kim 87 166 2020 10.1016/j.jprocont.2020.02.003 A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system 

    상세보기

35. Computers & Chemical Engineering Kumar 150 2021 10.1016/j.compchemeng.2021.107291 Industrial, large-scale model predictive control with structured neural networks 

    상세보기

36. Computers & Chemical Engineering Lee 21 S873 1997 10.1016/S0098-1354(97)00159-2 Model predictive control for nonlinear batch processes with asymptotically perfect tracking 

    상세보기

37. Journal of Process Control Lee 13 607 2003 10.1016/S0959-1524(02)00096-3 Iterative learning control-based batch process control technique for integrated control of end product properties and transient profiles of process variables 

    상세보기

38. International Journal of Control, Automation and Systems Lee 2 263 2004 Approximate dynamic programming strategies and their applicability for process control: A review and future directions 

    원문보기 상세보기

39. Control Engineering Practice Lee 15 1306 2007 10.1016/j.conengprac.2006.11.013 Iterative learning control applied to batch processes: An overview 

    상세보기

40. Journal of Process Control Lee 19 859 2009 10.1016/j.jprocont.2008.11.009 An approximate dynamic programming based approach to dual adaptive control 

    상세보기

41. Industrial and Engineering Chemistry Research Lee 33 1530 1994 10.1021/ie00030a013 Extended Kalman filter based nonlinear model predictive control 

    상세보기

42. Journal of Process Control Lee 20 1038 2010 10.1016/j.jprocont.2010.06.007 Approximate dynamic programming approach for process control 

    상세보기

43. Applied Soft Computing Li 11 4488 2011 10.1016/j.asoc.2011.08.022 Reinforcement learning control with adaptive gain for a Saccharomyces cerevisiae fermentation process 

    상세보기

44. Lillicrap 2015 Continuous control with deep reinforcement learning 

45. Journal of Process Control Liu 24 95 2014 10.1016/j.jprocont.2014.07.002 Robust PID based indirect-type iterative learning control for batch processes with time-varying uncertainties 

    상세보기

46. Computers & Chemical Engineering Lu 154 2021 10.1016/j.compchemeng.2021.107491 BayesIan optimization with reference models: A case study in MPC for HVAC central plants 

    상세보기

47. Computers & Chemical Engineering Ma 142 2020 10.1016/j.compchemeng.2020.107016 Machine-learning-based simulation and fed-batch control of cyanobacterial-phycocyanin production in Plectonema by artificial neural network and deep reinforcement learning 

    상세보기

48. Journal of Process Control Ma 75 40 2019 10.1016/j.jprocont.2018.11.004 Continuous control of a polymerization system with deep reinforcement learning 

    상세보기

49. Annual Reviews in Control Mesbah 45 107 2018 10.1016/j.arcontrol.2017.11.001 Stochastic model predictive control with active uncertainty learning: A survey on dual control 

    상세보기

50. Mnih 1928 2016 International conference on machine learning Asynchronous methods for deep reinforcement learning 

51. Nature Mnih 518 529 2015 10.1038/nature14236 Human-level control through deep reinforcement learning 

    상세보기

52. Computers & Chemical Engineering Morari 23 667 1999 10.1016/S0098-1354(98)00301-9 Model predictive control: past, present and future 

    상세보기

53. Moyne 2018 Run-to-run control in semiconductor manufacturing 

54. Computers & Chemical Engineering Nian 2020 10.1016/j.compchemeng.2020.106886 A review on reinforcement learning: Introduction and applications in industrial process control 

    상세보기

55. Journal of Process Control Pandian 69 16 2018 10.1016/j.jprocont.2018.07.013 Control of a bioreactor using a new partially supervised reinforcement learning algorithm 

    상세보기

56. IFAC-PapersOnLine Paulson 51 523 2018 10.1016/j.ifacol.2018.11.036 Nonlinear model predictive control with explicit backoffs for stochastic systems under arbitrary uncertainty 

    상세보기

57. IEEE Control Systems Letters Paulson 4 719 2020 10.1109/LCSYS.2020.2980479 Approximate closed-loop robust model predictive control with guaranteed stability and constraint satisfaction 

    상세보기

58. IEEE Transactions on Control Systems Technology Peroni 13 786 2005 10.1109/TCST.2005.852105 Optimal control of a fed-batch bioreactor using simulation-based approximate dynamic programming 

    상세보기

59. Petsagkourakis 2020 Chance constrained policy optimization for process control and optimization 

60. IFAC-PapersOnLine Petsagkourakis 53 11264 2020 10.1016/j.ifacol.2020.12.361 Constrained reinforcement learning for dynamic optimization under uncertainty 

    상세보기

61. Computers & Chemical Engineering Petsagkourakis 133 2020 10.1016/j.compchemeng.2019.106649 Reinforcement learning for batch bioprocess optimization 

    상세보기

62. Control Engineering Practice Qin 11 733 2003 10.1016/S0967-0661(02)00186-7 A survey of industrial model predictive control technology 

    상세보기

63. AIChE Journal Quintana-Hernandez 50 1407 2004 10.1002/aic.10133 Mathematical modeling and kinetic parameter estimation in batch crystallization 

    상세보기

64. Automatica Rao 37 1619 2001 10.1016/S0005-1098(01)00115-7 Constrained linear state estimation-a moving horizon approach 

    상세보기

65. Chemical Engineering Research and Design Ren 164 102 2020 10.1016/j.cherd.2020.09.019 A batch-wise LSTM-encoder decoder network for batch process monitoring 

    상세보기

66. AIChE Journal Robertson 42 2209 1996 10.1002/aic.690420811 A moving horizon-based approach for least-squares estimation 

    상세보기

67. Journal of Process Control Shi 17 715 2007 10.1016/j.jprocont.2007.02.002 Single-cycle and multi-cycle generalized 2D model predictive iterative learning control (2D-GPILC) schemes for batch processes 

    상세보기

68. Computers & Chemical Engineering Shin 127 282 2019 10.1016/j.compchemeng.2019.05.029 Reinforcement learning-overview of recent progress and implications for process control 

    상세보기

69. IFAC-PapersOnLine Sorourifar 54 243 2021 10.1016/j.ifacol.2021.08.249 A data-driven automatic tuning method for MPC under uncertainty using constrained Bayesian optimization 

    상세보기

70. Sutton 2018 Reinforcement learning: an introduction 

71. IEEE Transactions on Automatic Control Van de Water 26 1080 1981 10.1109/TAC.1981.1102781 The certainty equivalence property in stochastic control theory 

    상세보기

72. Machine Learning Williams 8 229 1992 10.1007/BF00992696 Simple statistical gradient-following algorithms for connectionist reinforcement learning 

    상세보기

73. Computers & Chemical Engineering Wilson 21 S1233 1997 10.1016/S0098-1354(97)00218-4 Neuro-fuzzy modeling and control of a batch process involving simultaneous reaction and distillation 

    상세보기

74. Journal of Process Control Xiong 15 11 2005 10.1016/j.jprocont.2004.04.005 A batch-to-batch iterative optimal control strategy based on recurrent neural network models 

    상세보기

75. Xu 241 2020 2020 IEEE 9th data driven control and learning systems conference Iterative learning control (ILC) guided reinforcement learning control (RLC) scheme for batch processes 

76. Yang 3301 2017 IJCAI, vol. 17 Multi-task deep reinforcement learning for continuous action control 

77. Computers & Chemical Engineering Yoo 144 2021 10.1016/j.compchemeng.2020.107133 Reinforcement learning based optimal control of batch processes using Monte-Carlo deep deterministic policy gradient with phase segmentation 

    상세보기

78. Yoo 2020 Dynamic penalty function approach for constraints handling in reinforcement learning 

79. Transactions of the Institute of Measurement and Control Zhang 27 391 2005 10.1191/0142331205tm156oa A neural network-based strategy for the integrated batch-to-batch control and within-batch control of batch processes 

    상세보기

80. Zhang 314 2019 2019 IEEE conference on control technology and applications Optimization control of a fed-batch process using an improved reinforcement learning algorithm 

활용도 Top5 논문

더보기

해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.