1971[5] Kushner,H.J. Introduction to stochastic control. Holt, Rinehartand Winston, 1971.1983[24] T. Morozan, “Stabilization of some stochastic discrete-time control-systems,”Stochast. Anal. Applicat. , vol. 1, no. 1, pp. 89–116, 1983.1986[12] Robert FS.1986.Stochastic optimal control:theory and application[M].Wiley.[] Kumar,P.R.,&Varaiya,P. Stochastic systems : estimation, identification, and adaptive control. Prentice Hall.19861987[16] D.W. Clarke, C. Mothadi, and P.S. Tuffs. Generalized predictive control. Auto-matica , 23:137–160, 1987.1997[10] J. Birge and F. Louveaux, Introduction to Stochastic Programming[M]. Springer, New York, 1997.[9] McLachlan GJ, Thriyambakam Krishnan. The EM algorithm and extensions[M]. Wiley.1997.[6]Meadows ES.Dynamic Programming and Model Predictive Control[C].Proceedings of the American Control Conference,1635-1639.1997.[2] Lee J,Yu Z. Worst-case formulation of model predictive control for systems with bounded parameters[J].Automatica 33,763–781. 1997.1998[6] Schwarm A,Nikolaou M.1998.Chance constrained model predictive control[J].AIChE Journal,45,1743–1752.[21] J.H. Lee and B.L. Cooley. Optimal feedback control strategies for state-space systems with stochastic parameters. IEEE Trans. Autom. Control, 43(10):1469–1475, 1998.[14] P.O.M. Scokaert and D.Q. Mayne. Min-max feedback model predictive control for constrained linear systems. IEEE Trans. Autom. Control , 43:1136–1142, 1998.[25] Koroleva N. Robust stability of uncertain stochastic differential delay equations [J]. Systems & Control Letters,1998,35(9):325-336.[] Kamen, E. W., & Su, J. K. Introduction to optimal estimation. London, UK:Springer.1999. 1999[4] Bemporad A, Morari M. Robust model predictive control: A survey[J]. Robustness in Identification and Control, 1999, 245: 207-226.[5] Blanchini F. Set invariance in control[J]. Automaica, 1999,35(11): 1747-1767.2000[3] Mayne D Q, Rawlings J B, Rao C V. Constrained model predictive control: Stability and optimality[J]. Automatica, 2000,36(6): 789-814.2001[10] 杜晓宁，席裕庚，李少远．约束预测控制的一种快速算法[J]．上海交通大学学报，2001，35(11)：1624-1627．Du X N, Xi Y G, Li S Y. Fast algorithm of constrained model predictive control[J]. Journal of Shanghai Jiaotong University, 2001, 35(11): 1624-1627.[11] Chisci L, Rossiter J A, Zappa G. Systems with persistent disturbance: Predictive control with restricted constraints[J]. Automatica, 2001, 37(7): 1019-1028.[23] Gershon R,Shaked U. H ∞control and filtering of discrete-time stochastic systems with multiplicative noise[J].Automatica,2001,37(3):409-417.[] I. Batina, A. A. Stoorvogel, and S. Weiland. Stochastic disturbance rejection in model predictive control by randomized algorithms. In proceedings of American Control Conference, pages 732–737, 2001.[1] I. Batina, A. A. Stoorvogel, and S. Weiland. Feed-back model predictive control by randomized algorithms. In proceedings of European Control Conference, pages 3185–3189, 2001.[2] I. Batina, A. A. Stoorvogel, and S. Weiland. Model predictive control by randomized algorithms for systems with constrained inputs and stochastic disturbances. IEEE Transactions on Automatic Control, December 2001.[11] D. H. van Hessem, C. W. Scherer, and O. H. Bosgra, “LMI-based closed-loop economic optimization of stochastic process operation under state and input constraints,”in Proc. 40th IEEE Conf. Decision Control, Orlando, FL, 2001, pp. 4228–4233.2002[17] P. Li, M. Wendt, and G. Wozny. A probabilistically constrained model predictive controller. Automatica, 38(7):1171–1176, 2002.[3] Kouvaritakis B,Cannon M,Tsachouridis V. Recent developments in stochastic MPC and sustainable development[J].Annual Reviews in Control,2002,volume 28,23–35. 2002.[4] I. Batina, A. A. Stoorvogel, and S. Weiland. Optimal control of linear, stochastic systems with state and input constraints. In proceedings of European Control Conference, pages 1564–1569, 2002.[7] vanHessem,D.H.,&Bosgra,O.H. A conic reformulation of model predictive control including boundedand stochastic disturbances understate and input constraints. In Proc.41st IEEE conf.Decision control (pp.4643 4648), 2002.2003[9] Liu B, Xi Y G. An aggregation based robust model predictive controller[J]. Acta Automatic Sinica, 2003, 29(6): 801-808.[19] A.J. Felt. Stochastic linear model predictive control using nested decomposition.In Proc. American Control Conf., pages 3602–3607, 2003.[15] A. Bemporad, F. Borrelli, and M. Morari. Min-max control of constrained uncertain discrete-time linear systems. IEEE Trans. Automatic Control , 48(9):1600–1606, 2003.[] vanHessem,D.H.,&Bosgra,O.H. A full solution to the constrained stochastic closed-loop MPC problem via state and innovations feedback and its receding horizon implementation, In Proceedings of the 42nd IEEE conference on decision and control,vol.1,(pp.929–934).2003.2004[12] Foss S, Konstantopoulos T. An overview of some stochastic stability methods[J]. Journal of Operations Research of Japan, 2004, 47(4): 275-303.[18] I. Batina. Model predictive control for stochastic systems by randomized algorithms . PhD thesis, Technische Universiteit Eindhoven, 2004.[24] Zhang WeiHai. On stabilizability and exact observability of stochastic systems with their applications [J], Automatic,2004,40(7):87-94.[7] Munoz D de la Pena, Alamo T, Bemporad A, Camacho EF. 2004. A Dynamic Programming Approach for Determining the explicit solution of linear MPC controllers[C]. Proceedings of 43rd IEEE Conference on Decision and Control, 2479-2484.[1] Batina,I. Model predictive control for stochastic systems by randomized algorithms. Ph.D.thesis, Technische Universiteit Eindhoven,2004.[4] Kouvaritakis B., Cannon M., Tsachouridis V. Recent developments in stochastic MPC and sustainable development. Annual Reviewsin Control, 28(1),23 35. 2004.[15] J. Dupaˇcova, G. Consigli, and S. W. Wallace, “Scenarios for multistage stochastic programs,”Ann. Operat. Res., vol. 100, pp. 25–53, 2004.[24] Zhang WeiHai.On stabilizability and exact observability of stochastic systems with their applications[J],Automatic,40(7):87-94, 2004.2005[13] 方洋旺．随机系统最优控制[M]．北京：清华大学出版社，2005．Fang Y W. Optimal control for stochastic system[M]. Beijing: Tsinghua University Press, 2005.[4]Munoz D de la Pena,Bemporad A,Alamo T. Stochastic Programming Applied to Model Predictive Control[C].Proceeding of the 44th IEEE CDC,1361-1366.2005.[] Yan, J., & Bitmead, R. R. Incorporating state estimation into model predictive control and its application to network traffic control.Automatica, 595–604. 2005.2006[31] Couchman P, Kouvaritakis B, Cannon M. MPC on state space models with stochastic input map [C]. Decision and Control, 2006 45th IEEE Conference on. IEEE, 2006: 3216-3221.[20] B. Kouvaritakis, M. Cannon, and P. Couchman. MPC as a tool for sustain-able development integrated policy assessment. IEEE Trans. Autom. Control ,51(1):145–149, 2006.[47] Couchman PD,Cannon M,Kouvaritakis B. Stochastic MPC with inequality stability constraints [J]. Automatica, 2006, 42(6):2169-2174.Couchman, P., Cannon, M., & Kouvaritakis, B. (2006). Stochastic model predictive control with nonlinear models. Automatica , submitted for Publication.[3] Couchman P, Kouvaritakis B, Cannon, M. MPC on state space models with stochastic input map. InProc. 45th IEEE Conf. Decision and Control (pp.3216 3221).20062007[28] Cannon M, Couchman P, Kouvaritakis B. MPC for stochastic systems [J]. Assessment and Future Directions of Nonlinear Model Predictive Control, 2007: 255-268.[5] Cannon M,Couchman P,Kouvaritakis B. MPC for stochastic systems.Assessment and Future Directions of Nonlinear Model Predictive Control[J].Springer LNCIS,vol 358,255-268.2007.[11]Maciejowski JM,Visintini AL,Lygeros J. NMPC for Complex Stochastic Systems Using a Markov Chain Monte Carlo Approach [J]. Springer-Verlag, Assessment and Future Directions, LNCIS 358,pp.269-281. 2007.[3] Bertsimas, D., & Brown, D. B. (2007). Constrained stochastic LQC: a tractable approach. IEEE Transactions on Automatic Control, 52(10),1826–1841.[5]Primbs, J.A. A soft constraint approach to stochastic receding horizon control . InProc . 46th IEEE Conf.Decision Control. 2007.2008[26] Cannon M, Kouvaritakis B, Couchman P. [C]. Proceedings of 17th IFAC World Congress. 2008: 15321-15326.[9] Wang C, Ong C J, Sim M. Constrained linear system with disturbance: convergence under disturbance feedback.Au-tomatica, 2008, 44(10): 2583--2587[16] G. C. Goodwin, J.stergaard, D. E. Quevedo, and A. Feuer, “A vector quantization approach to scenario generation for stochastic NMPC,”in Int. Workshop Assess. Future Direct. Nonlinear Model Predict. Con-trol , Pavia, Italy, 2008.2009[11] Mark Cannon, Basil Kouvaritakis, Xingjian Wu, Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints, Automatica, V olume 45, Issue 1, January 2009, Pages 167-172[13] Cannon M, Kouvaritakis B, Ng D. Probabilistic tubes in linear stochastic model predictive control [J]. Systems & Control Letters, 2009, 58(10): 747-753.[4] Cannon M, Kouvaritakis B, Wu X J. Probabilistic constrained MPC for multiplicative and additive stochastic uncertainty.IEEE Transactions on Automatic Control,54(7),1626–1632. 2009[4] Cannon M. Ng, D,Kouvaritakis, B. Successive linearization NMPC for a class of stochastic nonlinear systems. InLNCIS: V ol. 384. Nonlinear model predictive control :towards new challenging applications (pp.249 262).2009.[6] Primbs J, Sung C H. Stochastic receding horizon control of constrained linear systems with state and control multiplicative noise[J]. IEEE Transactions on Automatic Control, 2009, 54(2):221-230.[8] Hokayem P, Chatterjee D, Lygeros J. On stochastic model predictive control with bounded control inputs[C]//Proceedings of the Combined 48th IEEE Conference on Decision & Control and 28th Chinese Control Conference. Piscataway, NJ, USA: IEEE, 2009: 6359-6364.[8]Wang C, Ong C J, Sim M. Convergence properties of con-strained linear system under MPC control law using a±nedisturbance feedback.Automatica, 2009, 45(7): 1715-1720.[] Wang, Y., & Boyd, S. (2009). Performance bounds for linear stochastic control. SystemsandControlLetters, 58(3),178–182.[] Magni.L.,Pala.D,Scattolini,R.Stochastic model predictive control of constrained linear systems with additive uncertainty. In European control conference (pp.2235–2240).Budapest, Hungary, 2009.[] Eugenio.E,Agarwal.M,Chatterjee,D.Lygeros,J. Convexity and convex approximations of discete-time stochastic control problems with constraints. Automatica, 47(9), 2082–2087.2011.[8] Chatterjee, D., Hokayem, P., & Lygeros, J. Stochastic receding horizon control with bounded control inputs: a vector space approach. IEEE Transactions on AutomaticControl,2009.[2] Bernardini D,Bemporad A. Scenario-based model predictive control of stochastic constrained linear systems. In IEEE conference on decision and control (pp.6333–6338).Shanghai,China.2009[4] Oldewurter,F.,Jones,C.N.,Morari,M..A tractable approximation of chance constrained stochastic MPC based on affine disturbance feedback . InProc.48th IEEE Conf. Decision Control.Cancun .2009.2010[27] Kouvaritakis B, Cannon M, RakovićS V, et al. Explicit use of probabilistic distributions in linear predictive control [J]. Automatica, 2010, 46(10): 1719-1724.[10]Wang C, Ong C J, Sim M. Model predictive control using segregated disturbance feedback. IEEE Transactions on Automatic Control, 2010, 55(4): 831--840[48] Minyong Shin.Primbs constrained stochastic MPC under multiplicative noise for financial applications[C].49th IEEE Conference on Decision and Control,Atlanta,2010,6101-6106.[] Ramponi, F., Chatterjee, D., Milias-Argeitis, A., Hokayem, P., & Lygeros, J. Attaining mean square boundedness of a marginally stable stochastic linear system with a bounded control input.IEEE Transactions on Automatic Control,55(10),2414–2418. 2010.[] Hokayem,P.,Chatterjee,D.,Ramponi,F.,Chaloulos,G.,Lygeros,J. Stable stochastic receding horizon control of linear systems with bounded control inputs. InInt. Symposium on mathematical theory of networks and systems (MTNS) (pp.31–36, Budapest).Hungary, July 2010.[] Hokayem, P., Cinquemani, E., Chatterjee. D,Lygeros, J. Stochastic MPC with imperfect state information and bounded controls .In Proceedings of the UKACC int. Conference on control. Coventry, UK, Sept.2010.[20] A. Bemporad, T. Gabbriellini, L. Puglia, and L. Bellucci, “Scenario-based stochastic model predictive control for dynamic option hedging,”in Proc. 49th IEEE Conf. Decision Control , Atlanta, GA, 2010, pp. 6089–6094[19] D. Bernardini, M. C. F. Donkers, A. Bemporad, and W. P. M. H.Heemels, “A model predictive control approach for stochastic networked control systems,”inProc. 2nd IFAC Workshop Estimation and Control Netw. Syst. , Annecy, France, 2010, pp. 7–12.[18] M. Bichi, G. Ripaccioli, S. Di Cairano, D. Bernardini, A. Bemporad, and I. Kolmanovsky, “Stochastic model predictive control with driver behavior learning for improved powertrain control,”in Proc. 49th IEEE Conf. Decision Control, Atlanta, GA, 2010, pp. 607–6082.[17] G. Ripaccioli, D. Bernardini, S. Di Cairano, A. Bemporad, and I. V. Kolmanovsky, “A stochastic model predictive control approach for series hybrid electric vehicle power management,”in Proc. Amer. Contr.Conf., Baltimore, MD, 2010, pp. 5844–5849.2011[7] Chatterjee D, Hokayem P, Lygeros J. Stochastic receding horizon control with boundedcontrol inputs –A vector space approach[J]. IEEE Transactions on Automatic Control, 2011,56(11): 2704-2710.[5]Cannon M, Kouvaritakis B, Rakovic S V, Cheng Q F. Stochastic tubes in model predictive control with probabilistic constraints. IEEE Transactions on Automatic Control,56(1),194-200, 2011[7]Wen J W, Liu F. Receding horizon control for constrained Markovian jump linear systems with bounded disturbance.ournal of Dynamic Systems, Measurement, and Control,2011[2]Su Y, Tan K K, Lee T H. Comments on \ Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints. Automatica, 2011[9] Eugenio.E,Agarwal.M,Chatterjee,D.Lygeros,J. Convexity and convex approximations of discete-time stochastic control problems with constraints. Automatica, 47(9), 2082–2087.2011. 2012[11] Hokayem P, Cinquemani E, Chatterjee D, Ramponi F, Lygeros J. Stochastic receding horizon control with output feedback and bounded controls. Automatica, 2012, 48(1):77--88[6] Bernardini D, Bemporad A. Stabilizing model predic tive control of stochastic constrained linear systems. IEEE Transactions on Automatic Control, 2012[50] Mark Cannon, Cheng QiFeng.Stochastic tube MPC with state estimation[J].Automatica, 2012,48 (7):536-5412013[5]Oldewurtel.F,Sturzenegger.D,Esfahani.P.M,Andersson.G,Morari.M,Lygeros.J. A daptively Constrained Stochastic Model Predictive Control for Closed-Loop Constraint Satisfaction . American Control Conference (ACC), pages 4674 - 4681,2013冯德兴.1995.凸分析基础[M].北京：科学出版社.方洋旺.2005.随机系统最优控制[M].北京：清华大学出版社.郭尚来.1999.随机控制[M].北京：清华大学出版社.钱积新，赵均，徐祖华.2007.预测控制[M].北京：化学工业出版社.王金德.1990.随机规划[M].南京：南京大学出版社.席裕庚.1993.预测控制[M].北京：国防工业出版社.。