Postdecision-state-based approximate dynamic programming for robust predictive control of constrained stochastic processes

Industrial and Engineering Chemistry Research

Volumn 50, Issue 3, 2011, Pages 1389-1399

  Wong, Wee Chin ^a   Lee, Jay H ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADVANCED PROCESS CONTROL; APPROXIMATE DYNAMIC PROGRAMMING; CERTAINTY EQUIVALENCE; COMPUTATIONALLY EFFICIENT; CONSTRAINT BOUNDARIES; CONTROL PROBLEMS; DETERMINISTIC PROBLEMS; EXOGENOUS DISTURBANCES; IN-PROCESS CONTROL; OPTIMIZATION SOLVERS; ROBUST PREDICTIVE CONTROL; STATE-BASED; STOCHASTIC PROBLEMS; STOCHASTIC PROCESS;

DYNAMIC PROGRAMMING; INTELLIGENT CONTROL; PARAMETER ESTIMATION; PREDICTIVE CONTROL SYSTEMS; QUALITY CONTROL; RANDOM PROCESSES; STOCHASTIC SYSTEMS;

MODEL PREDICTIVE CONTROL;

EID: 79952801020     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie1007882     Document Type: Article

References (35)

1. Morari, M.; Lee, J. H. Model predictive control: Past, present and future Comput. Chem. Eng. 1999, 23, 667-682 (Pubitemid 29210323)
2. Scokaert, P.; Mayne, D. Min-max feedback model predictive control for constrained linear systems IEEE Trans. Autom. Control 1998, 43, 1136-1142 (Pubitemid 128737398)
3. Lee, J. H.; Yu, Z. Worst-case formulations of model predictive control Automatica 1997, 33, 763-781
4. Bemporad, A.; Morari, M. Robust moidel predictive control: A survey. In Robustness in Identification and Control; Lecture Notes in Control and Information Sciences, Vol. 245; Springer Berlin: Heidelberg, Germany, 1999.
5. Laird, C. D.; Biegler, L. T. Large-scale nonlinear programming for multi-scenario optimization. In Modeling, Simulation and Optimization of Complex Processes; Bock, H. G.; Kostina, E.; Phu, H. X.; Rannacher, R., Eds.; Springer: Heidelberg, Germany, 2008; pp 323 - 336.
6. Pena, D. M. d. l.; Bemporad, A.; Alamo, T. Stochastic Programming Applied to Model Predictive Control; 44th IEEE Conference on Decision and Control, and the European Control Conference, Seville, Spain, 2005; IEEE: Piscataway, NJ, 2005; pp 1361 - 1366.
7. Arkun, Y.; Stephanopoulos, G. Studies in the synthesis of control structures for chemical pro-cesses. Part IV. Design of steady-state optimizing control structures for chemical process units AIChE J. 1980, 26, 975-991 (Pubitemid 11461002)
8. L.T. Narraway, J. P.; Barton, G. Interaction between process design and process control: economic analysis of process dynamics J. Process Control 1991, 1, 243-250
9. Loeblein, C.; Perkins, J. D. Structural design for on-line process optimization: I. dynamic economics of MPC AIChE J. 1999, 45, 1018
10. Feldbaum, A. Dual control theory, part I Autom. Remote Control 1960, 21, 874-880
11. Feldbaum, A. Dual control theory part III Autom. Remote Control 1960, 21, 1453-1464
12. Lee, J. M.; Lee, J. H. An approximate dynamic programming approach based approach to dual adaptive control J. Process Control 2009, 19, 859-864
13. Hovd, M.; Bitmead, R. R. Interaction between Control and State Estimation in Nonlinear MPC. Presented at the 7th International symposium on dynamics & control of process systems, Boston, MA, 2004.
14. Roy, B. V.; Bertsekas, D. P.; Lee, Y.; Tsitsiklis, J. A neuro-dynamic programming approach to retailer inventory management IEEE Conf. Decis. Control 1997, 4052-4057
15. Powell, W. B. What You Should Know about Dynamic Programming; Technical Report, Wiley Periodicals, Inc., Naval Research Logistics, 2009.
16. Bellman, R. E. Dynamic Programming; Princeton University Press: Princeton, NJ, 1957.
17. Lee, J. M.; Lee, J. H. Simulation-based learning of cost-to-go for control of nonlinear processes Korean J. Chem. Eng. 2004, 21, 338-344 (Pubitemid 38770638)
18. Lee, J. M.; Lee, J. H. Approximate dynamic programming-based approaches for input-ouptut data-driven control of nonlinear processes Automatica 2005, 41, 1281-1288 (Pubitemid 40644867)
19. Lee, J. H.; Lee, J. M. Approximate dynamic programming based approach to process control and scheduling Comput. Chem. Eng. 2006, 30, 1603-1618 (Pubitemid 44292273)
20. Lee, J. M.; Lee, J. H. Value function-based approach to the scheduling of multiple controllers J. Process Control 2008, 18, 533-542
21. Lee, J. M.; Kaisare, N. S.; Lee, J. H. Choice of approximator and design of penalty function for an approximate dynamic programming based control approach J. Process Control 2006, 16, 135-156 (Pubitemid 41497892)
22. Powell, W. B. Approximate Dynamic Programming: Solving the Curses of Dimensionality; Wiley Series in Probability and Statistics; Wiley-Interscience: New York, 2007.
23. Ma, J.; Powell, W. A convergent recursive least squares policy iteration algorithm for multidimensional Markov decision process with continuous state and action spaces. Presented at the IEEE Conference on Approximate Dynamic Programming and Reinforcement Learning (part of IEEE Symposium on Computational Intelligence), Nashville, TN, 2009.
24. Tsitsiklis, J.; Roy, B. V. Feature-based methods for large scale dynamic programming Machine Learning Journal 1996, 22, 59-94 (Pubitemid 126724363)
25. Konidaris, G.; Osentoski, S. Value function approximation in reinforcement learning using the Fourier basis; Technical Report, 2008.
26. Tesauro, G. Practical issues in tempora-difference learning Mach. Learn. J. 1992, 8, 257-277
27. Thrun, S.; Schwartz, A. Issues in Using Function Approximation for Reinforcement Learning; Fourth Connectionist Models Summer School: Hillsdale, NJ, 1993.
28. Sabes, P. Approximating Q-Values with Basis Function Representations; Fourth Connectionist Models Summer School: Hillsdale, NJ, 1993.
29. Boyan, J. A.; Moore, A. W. Generalization in Reinforcement Learning: Safely Approximating the Value Function Adv. Neural Inf. Process. Syst. 7 1995, 369-376
30. Bradtke, S. Reinforcement learning applied to linear quadratic regulation Adv. Neural Inf. Process. Syst. 1993, 295-302
31. Ormoneit, D.; Sen, S. Kernel-based reinforcement learning Mach. Learn. J. 2002, 49, 161-178 (Pubitemid 34325684)
32. Gordon, G. J. Stable Function Approximation in Dynamic Programming; Technical Report, Carnegie Mellon: Pittsburgh, PA, 1995.
33. Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statisical Learning: Data Mining, Inference and Prediction, 2 nd ed.; Springer-Verlag: New York, 2008.
34. Smart, W.; Kaelbling, L. Practical reinforcement learning in continuous spaces 17th Int. Conf. Mach. Learn. 2000, pp 903-910
35. Batina, I. Ph. D. thesis, Technische Universiteit Eindhoven: The Netherlands, 2004.