Stochastic model predictive control with active uncertainty learning: A Survey on dual control

Annual Reviews in Control - Tập 45 - Trang 107-117 - 2018
Ali Mesbah1
1Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA, 94720, USA

Tài liệu tham khảo

Aggelogiannaki, 2006, Multiobjective constrained MPC with simultaneous closed-loop identification, International Journal of Adaptive Control and Signal Processing, 20, 145, 10.1002/acs.892 Allison, 1995, Dual adaptive control of chip refiner motor load, Automatica, 31, 1169, 10.1016/0005-1098(95)00030-Z Alspach, 1972, Dual control based on approximate a posteriori density functions, IEEE Transactions on Automatic Control, 17, 689, 10.1109/TAC.1972.1100099 Alster, 1974, A technique for dual adaptive control, Automatica, 10, 627, 10.1016/0005-1098(74)90083-1 Anderson, 1985, Adaptive systems, lack of persistency of excitation and bursting phenomena, Automatica, 21, 247, 10.1016/0005-1098(85)90058-5 Åström, 1971, Problems of identification and control, Journal of Mathematical Analysis and Applications, 34, 90, 10.1016/0022-247X(71)90161-2 Åström, 1983, Theory and applications of adaptive controla, Automatica, 19, 471, 10.1016/0005-1098(83)90002-X Åström, 2008 Bar-Shalom, 1974, Dual effect, certainty equivalence, and separation in stochastic control, IEEE Transactions on Automatic Control, 19, 494, 10.1109/TAC.1974.1100635 Bar-Shalom, 1976, Concepts and methods in stochastic control, 99, 10.1016/B978-0-12-012712-2.50009-3 Bavdekar, V., Ehlinger, V., Gidon, D., & Mesbah, A. (2016). Stochastic predictive control with adaptive model maintenance. In Proceedings of the 55th IEEE Conference on Decision and Control (pp. 2745–2750). Las Vegas. Bavdekar, V., & Mesbah, A. (2016). Stochastic model predictive control with integrated experiment design for nonlinear systems. In Proceedings of the 11th IFAC Symposium on Dynamics and Control of Process Systems (DYCOPS) (pp. 49–54). Trondheim, Norway. Bayard, 1991, A forward method for optimal stochastic nonlinear and adaptive control, IEEE Transactions on Automatic Control, 36, 1046, 10.1109/9.83535 Bayard, 1992, Reduced complexity dynamic programming based on policy iteration, Journal of Mathematical Analysis and Applications, 170, 75, 10.1016/0022-247X(92)90007-Z Bayard, 1985, Implicit dual control for general stochastic systems, Optimal Control Applications and Methods, 6, 265, 10.1002/oca.4660060307 Bayard, 2010, Implicit dual control based on particle filtering and forward dynamic programming, International Journal of Adaptive Control and Signal Processing, 24, 155 Bellman, 1957 Bemporad, 1999, Robust model predictive control: A survey, 207 Ben-Tal, 2009 Bertsekas, 2000 Bertsekas, 2005, Dynamic programming and suboptimal control: A survey from ADP to MPC, European Journal of Control, 11, 310, 10.3166/ejc.11.310-334 Bertsekas, D. P., & Tsitsiklis, J. N. (1995). Neuro-dynamic programming: An overview. In Proceedings of the 34th IEEE Conference on Decision and Control (pp. 560–564). New Orleans. Bertsimas, 2011, Theory and applications of robust optimization, SIAM Review, 53, 464, 10.1137/080734510 Birmiwal, 1984, Dual control guidance for simultaneous identification and interception, Automatica, 20, 737, 10.1016/0005-1098(84)90083-9 Bombois, 2006, Least costly identification experiment for control, Automatica, 42, 1651, 10.1016/j.automatica.2006.05.016 Bugeja, 2009, Dual adaptive dynamic control of mobile robots using neural networks, IEEE Transactions on Systems, Man, and Cybernetics, Part B, 39, 129, 10.1109/TSMCB.2008.2002851 Chen, 2003, Bayesian filtering: From kalman filters to particle filters, and beyond, Statistics, 182, 1, 10.1080/02331880309257 Curry, 1969, A new algorithm for suboptimal stochastic control, IEEE Transactions on Automatic Control, 14, 533, 10.1109/TAC.1969.1099243 Doob, 1953 Dreyfus, 1964, Some types of optimal control of stochastic systems, Journal of the Society for Industrial and Applied Mathematics, Series A: Control, 2, 120, 10.1137/0302010 Dumont, 1988, Wood chip refiner control, IEEE Control Systems, 8, 38, 10.1109/37.1872 Dumont, G. A., & Huzmezan, M. (2002). Concepts, methods and techniques in adaptive control. In Proceedings of the American Control Conference (pp. 1137–1150). Anchorage, Alaska. de Farias, 2003, The linear programming approach to approximate dynamic programming, Operations Research, 51, 850, 10.1287/opre.51.6.850.24925 Farina, 2016, Stochastic linear model predictive control with chance constraints–a review, Journal of Process Control, 44, 53, 10.1016/j.jprocont.2016.03.005 Feldbaum, 1960, Dual-control theory. i, Automatic Remote Control, 21, 874 Feldbaum, 1960, Dual-control theory. II, Automatic Remote Control, 21, 1033 Feldbaum, 1961, Dual-control theory. III, Automatic Remote Control, 22, 1 Feldbaum, 1961, Dual-control theory. IV, Automatic Remote Control, 22, 109 Feldbaum, 1965 Feng, X., & Houska, B. (2016). Real-time algorithm for self-reflective model predictive control. arXiv:1611.02408v1. Ferramosca, 2010, MPC For tracking zone regions, Journal of Process Control, 20, 506, 10.1016/j.jprocont.2010.02.005 Filatov, 1996, Dual control for an unstable mechanical plant, IEEE Control Systems Technology, 16, 31, 10.1109/37.526913 Filatov, 1995, Adaptive predictive control policy for nonlinear stochastic systems, IEEE Transactions on Automatic Control, 40, 1943, 10.1109/9.471221 Filatov, 2000, Survey of adaptive dual control methods, IEE Proceedings -Control Theory and Applications, 147, 118, 10.1049/ip-cta:20000107 Filatov, 2004 Filatov, 1997, Dual pole-placement controller with direct adaptation, Automatica, 33, 113, 10.1016/S0005-1098(96)00150-1 Flıdr, M., & Šimandl, M. (2013). Implicit dual controller based on stochastic integration rule. In Proceedings of the European Control Conference (pp. 896–901). Zürich, Switzerland. Forgione, 2015, Data-driven model improvement for model-based control, Automatica, 52, 118, 10.1016/j.automatica.2014.11.006 Forssell, 2000, Some results on optimal experiment design, Automatica, 36, 749, 10.1016/S0005-1098(99)00205-8 Geletu, 2013, Advances and applications of chance-constrained approaches to systems optimisation under uncertainty, International Journal of Systems Science, 44, 1209, 10.1080/00207721.2012.670310 Genceli, 1996, New approach to constrained predictive control with simultaneous model identification, AIChE Journal, 42, 2857, 10.1002/aic.690421015 Genz, 1998, Stochastic integration rules for infinite regions, SIAM Journal on Scientific Computing, 19, 426, 10.1137/S1064827595286803 Gevers, 2005, Identification for control: From the early achievements to the revival of experiment design, European Journal of Control, 11, 335, 10.3166/ejc.11.335-352 Gevers, 1986, Optimal experiment designs with respect to the intended model application, Automatica, 22, 543, 10.1016/0005-1098(86)90064-6 González, 2014, Model predictive control suitable for closed-loop re-identification, Systems & Control Letters, 69, 23, 10.1016/j.sysconle.2014.03.007 Goodwin, 1977 Goodwin, 1984 Goulart, 2006, Optimization over state feedback policies for robust control with constraints, Automatica, 42, 523, 10.1016/j.automatica.2005.08.023 Heirung, 2015, MPC-Based dual control with online experiment design, Journal of Process Control, 32, 64, 10.1016/j.jprocont.2015.04.012 Heirung, T. A. N., & Mesbah, A. (2017). Stochastic predictive control with autonomous model adaptation for model structure uncertainty. In Proceedings of the chemical process control conference (CPC). Tucson. Heirung, 2017, Stochastic model predictive control - what does it do?, Computers & Chemical Engineering, Accepted Heirung, 2017, Dual adaptive model predictive control, Automatica, 80, 340, 10.1016/j.automatica.2017.01.030 Hernandez, 2016, Persistently exciting tube MPC, 948 Hjalmarsson, 2005, From experiment design to closed-loop control, Automatica, 41, 393, 10.1016/j.automatica.2004.11.021 Hjalmarsson, 2008, Closed loop experiment design for linear time invariant dynamical systems via LMIs, Automatica, 44, 623, 10.1016/j.automatica.2007.06.022 Hokayem, 2012, Stochastic receding horizon control with output feedback and bounded controls, Automatica, 48, 77, 10.1016/j.automatica.2011.09.048 Houska, B., Telen, D., Logist, F., & Impe, J. V. (2016). Self-reflective model predictive control. arXiv:1610.03228v1. Hovd, M., & Bitmead, R. R. (2004). Interaction between control and state estimation in nonlinear MPC. In Proceedings of the 7th IFAC Symposium on Dynamics and Control of Process Systems (DYCOPS) (pp. 119–124). Cambridge, Massachusetts. Ismail, 2003, Dual adaptive control of paper coating, IEEE Transactions on Control Systems Technology, 11, 289, 10.1109/TCST.2002.806445 Kendrick, 1981 Kolmanovsky, 1998, Theory and computation of disturbance invariant sets for discrete-time linear systems, Mathematical Problems in Engineering, 4, 317, 10.1155/S1024123X98000866 Konda, 2003, On actor-critic algorithms, SIAM journal on Control and Optimization, 42, 1143, 10.1137/S0363012901385691 Kouvaritakis, 2016 Kulcsár, 1996, Dual control of linearly parameterised models via prediction of posterior densities, European Journal of Control, 2, 135, 10.1016/S0947-3580(96)70037-7 Kumar, K., Heirung, T. A. N., Patwardhan, S. C., & Foss, B. (2015). Experimental evaluation of a MIMO adaptive dual MPC. In Proceedings of the 9th IFAC International Symposium on Advanced Control of Chemical Processes (pp. 545–550). Whistler, Canada. Kumar, 2016 Kwakernaak, 1965, On-line iterative optimization of stochastic control systems, Automatica, 2, 195, 10.1016/0005-1098(65)90010-5 La, H. C., Potschka, A., Schlöder, J. P., & Bock, H. G. (2016). Dual control and information gain in controlling uncertain processes. In Proceedings of the 11th IFAC Symposium on Dynamics and Control of Process Systems (DYCOPS) (pp. 139–144). Trondheim, Norway. La, 2017, Dual control and online optimal experimental design, SIAM Journal on Scientific Computing, 39, 640, 10.1137/16M1069936 Landau, 2011 Larsson, C., Annergren, M., Hjalmarsson, H., Rojas, C., Bombois, X., Mesbah, A., & Moden, P. (2013). Model predictive control with integrated experiment design for output error systems. In Proceedings of the European Control Conference (pp. 3790–3795). Zürich. Larsson, C. A., Annergren, M., & Hjalmarsson, H. (2011). On optimal input design in system identification for model predictive control. In Proceedings of the 50th IEEE Conference on Decision and Control (pp. 805–810). Orlando. Larsson, 2016, An application-oriented approach to dual control with excitation for closed-loop identification, European Journal of Control, 29, 1, 10.1016/j.ejcon.2016.03.001 Larsson, 2015, Experimental evaluation of model predictive control with excitation (MPC-x) on an industrial depropanizer, Journal of Process Control, 31, 1, 10.1016/j.jprocont.2015.03.011 Lee, 2011, Model predictive control: Review of the three decades of development, International Journal of Control, Automation and Systems, 9, 415, 10.1007/s12555-011-0300-6 Lee, 2006, Choice of approximator and design of penalty function for an approximate dynamic programming based control approach, Journal of Process Control, 16, 135, 10.1016/j.jprocont.2005.04.010 Lee, 2009, An approximate dynamic programming based approach to dual adaptive control, Journal of Process Control, 19, 859, 10.1016/j.jprocont.2008.11.009 Lewis, 2008 Li, 2002, A probabilistically constrained model predictive controller, Automatica, 38, 1171, 10.1016/S0005-1098(02)00002-X Lindqvist, 2001, Identification for control: Adaptive input design using convex optimization, 4326 Ljung, 1999 Luus, 2000 MacGregor, 2012, Monitoring, fault diagnosis, fault-tolerant control and optimization: Data driven methods, Computers & Chemical Engineering, 47, 111, 10.1016/j.compchemeng.2012.06.017 MacRae, 1975, An adaptive learning rule for multiperiod decision problems, Econometrica: Journal of the Econometric Society, 43, 893, 10.2307/1911332 Marafioti, 2014, Persistently exciting model predictive control, International Journal of Adaptive Control and Signal Processing, 28, 536, 10.1002/acs.2414 Mayne, 2014, Model predictive control: Recent developments and future promise, Automatica, 50, 2967, 10.1016/j.automatica.2014.10.128 Mayne, 2005, Robust model predictive control of constrained linear systems with bounded disturbances, Automatica, 41, 219, 10.1016/j.automatica.2004.08.019 Mesbah, 2016, Stochastic model predictive control: An overview and perspectives for future research, IEEE Control Systems, 36, 30, 10.1109/MCS.2016.2602087 Mesbah, 2015, Least costly closed-loop performance diagnosis and plant re-identification, International Journal of Control, 1 Mesbah, A., & Streif, S. (2015). A probabilistic approach to robust optimal experiment design with chance constraints. In Proceedings of the 9th IFAC International Symposium on Advanced Control of Chemical Processes (pp. 100–105). Whistler, Canada. Milito, 1982, An innovations approach to dual control, IEEE Transactions on Automatic Control, 27, 132, 10.1109/TAC.1982.1102863 Morari, 1999, Model predictive control: Past, present and future, Computers & Chemical Engineering, 23, 667, 10.1016/S0098-1354(98)00301-9 Nemirovski, 2006, Convex approximations of chance constrained programs, SIAM Journal on Optimization, 17, 969, 10.1137/050622328 Oldewurtel, 2008, A tractable approximation of chance constrained stochastic MPC based on affine disturbance feedback, 4731 Patchell, 1971, Separability, neutrality and certainty equivalence, International Journal of Control, 13, 337, 10.1080/00207177108931948 Patwardhan, 2002, Issues in performance diagnostics of model-based controllers, Journal of Process Control, 12, 413, 10.1016/S0959-1524(01)00043-9 Paulson, 2017, Stochastic predictive control with joint chance constraints, International Journal of Control, 1 Paulson, 2015, Stability for receding-horizon stochastic model predictive control, 937 Powell, 2007 Powell, 2014, Clearing the jungle of stochastic optimization, 109 Pronzato, 2008, Optimal experimental design and some related control problems, Automatica, 44, 303, 10.1016/j.automatica.2007.05.016 Pronzato, 1996, An actively adaptive control policy for linear models, IEEE Transactions on Automatic Control, 41, 855, 10.1109/9.506238 Qin, 2012, Survey on data-driven industrial process monitoring and diagnosis, Annual Reviews in Control, 36, 220, 10.1016/j.arcontrol.2012.09.004 RADENKOVIĆ, 1988, Convergence of the generalized dual control algorithm, International Journal of Control, 47, 1419, 10.1080/00207178808906105 Rathouskỳ, 2013, MPC-Based approximate dual controller by information matrix maximization, International Journal of Adaptive Control and Signal Processing, 27, 974, 10.1002/acs.2370 Rojas, C. R., Katselis, D., Hjalmarsson, H., Hildebrand, R., & Bengtsson, M. (2011). Chance constrained input design. In Proceedings of 50th IEEE Conference on Decision and Control (pp. 2957–2962). Orlando. Sastry, 2011 Sehr, M. A., & Bitmead, R. R. (2017). Stochastic model predictive control: Output-feedback, duality and guaranteed performance. arXiv:1706.00733. Shapiro, 2009 Shouche, 1998, Simultaneous constrained model predictive control and identification of DARX processes, Automatica, 34, 1521, 10.1016/S0005-1098(98)80005-8 Shouche, 2002, Effect of on-line optimization techniques on model predictive control and identification (mpci), Computers & Chemical Engineering, 26, 1241, 10.1016/S0098-1354(02)00091-1 Si, 2004, 2 Stengel, 1986 Sternby, 1976, A simple dual control problem with an analytical solution, IEEE Transactions on Automatic Control, 21, 840, 10.1109/TAC.1976.1101383 Telen, 2017, A study of integrated experiment design for NMPC applied to the droop model, Chemical Engineering Science, 160, 370, 10.1016/j.ces.2016.10.046 Thangavel, 2015, Towards dual robust nonlinear model predictive control: A multi-stage approach, 428 Thompson, 2005, Stochastic iterative dynamic programming: A monte carlo approach to dual control, Automatica, 41, 767, 10.1016/j.automatica.2004.12.003 Tse, 1973, An actively adaptive control for linear systems with random parameters via the dual control approach, IEEE Transactions on Automatic Control, 18, 109, 10.1109/TAC.1973.1100242 Tse, 1976, Actively adaptive control for nonlinear stochastic systems, Proceedings of the IEEE, 64, 1172, 10.1109/PROC.1976.10288 Tse, 1973, Wide-sense adaptive dual control of stochastie nonlinear systems, IEEE Transactions on Automatic Control, 18, 98, 10.1109/TAC.1973.1100238 Wenk, 1980, A multiple model adaptive dual control algorithm for stochastic systems with unknown parameters, IEEE Transactions on Automatic Control, 25, 703, 10.1109/TAC.1980.1102417 Witsenhausen, 1971, Separation of estimation and control for discrete time systems, Proceedings of the IEEE, 59, 1557, 10.1109/PROC.1971.8488 Wittenmark, 1975, An active suboptimal dual controller for systems with stochastic parameters, Automatic Control Theory and Application, 3, 13 Wittenmark, 1975, Stochastic adaptive control methods: A survey, International Journal of Control, 21, 705, 10.1080/00207177508922026 Wittenmark, 1995, Adaptive dual control methods: An overview, 67 Wonham, 1968, On the separation theorem of stochastic control, SIAM Journal on Control, 6, 312, 10.1137/0306023 Žáčeková, 2013, Persistent excitation condition within the dual control framework, Journal of Process Control, 23, 1270, 10.1016/j.jprocont.2013.08.004 Zagrobelny, 2013, Quis custodiet ipsos custodes?, Annual Reviews in Control, 37, 260, 10.1016/j.arcontrol.2013.09.005