Model Predictive Control——Status and Challenges
-
摘要: 30多年来, 模型预测控制(Model predictive control, MPC)的理论和技术得到了长足的发展, 但面对经济社会迅速发展对约束优化控制提出的不断增长的要求, 现有的模型预测控制理论和技术仍面临着巨大挑战. 本文简要回顾了预测控制理论和工业应用的发展, 分析了现有理论和技术所存在的局限性, 提出需要加强预测控制的科学性、有效性、易用性和非线性研究. 文中简要综述了近年来预测控制研究和应用领域发展的新动向, 并指出了研究大系统、快速系统、低成本系统和非线性系统的预测控制对进一步发展预测控制理论和拓宽其应用范围的意义.Abstract: Since last 30 years the theory and technology of model predictive control (MPC) have been developed rapidly. However, facing to the increasing requirements on the constrained optimization control arising from the rapid development of economy and society, the current MPC theory and technology are still faced with great challenges. In this paper, the development of MPC theory and industrial applications is briefly reviewed and the limitations of current MPC theory and technology are analyzed. The necessity to strengthen the MPC research around scientificity, effectiveness, applicability and nonlinearity is pointed out. We briefly summarize recent developments and new trends in the area of MPC theoretical study and applications, and point out that to study the MPC for large scale systems, fast systems, low cost systems and nonlinear systems, will be significant for further development of MPC theory and broadening MPC application fields.
-
[1] Qin S J, Badgwell T A. A survey of industrial model predictive control technology. Control Engineering Practice, 2003, 11(7): 733-764[2] Mayne D Q, Rawlings J B, Rao C V, Scokaert P O M. Constrained model predictive control: stability and optimality. Automatica, 2000, 36(6): 789-814[3] Morari M, Bari#263; M. Recent developments in the control of constrained hybrid systems. Computers and Chemical Engineering, 2006, 30(10-12): 1619-1631[4] Wang W L, Rivera D E, Kempf K G. Model predictive control strategies for supply chain management in semiconductor manufacturing. International Journal of Production Economics, 2007, 107(1): 56-77[5] Dufour P, Michaud D J, Touré Y, Dhurjati P S. A partial differential equation model predictive control strategy: application to autoclave composite processing. Computers and Chemical Engineering, 2004, 28(4): 545-556[6] Salsbury T, Mhaskar P, Qin S J. Predictive control methods to improve energy efficiency and reduce demand in buildings. Computers and Chemical Engineering, 2013, 51: 77-85[7] Bryds M A, Grochowski M, Gminski T, Konarczak K, Drewa M. Hierarchical predictive control of integrated wastewater treatment systems. Control Engineering Practice, 2008, 16(6): 751-767[8] Keviczky T, Balas G J. Receding horizon control of an F-16 aircraft: a comparative study. Control Engineering Practice, 2006, 14(9): 1023-1033[9] Silani E, Lovera M. Magnetic spacecraft attitude control: a survey and some new results. Control Engineering Practice, 2005, 13(3): 357-371[10] Hovorka R, Canonico V, Chassin L J, Haueter U, Massi-Benedetti M, Federici M O, Pieber T R, Schaller H C, Schaupp L, Vering T, Wilinska M E. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiological Measurement, 2004, 25(4): 905-920[11] Morari M, Lee J H. Model predictive control: past, present and future. Computers and Chemical Engineering, 1999, 23(4-5): 667-682[12] Henson M A. Nonlinear model predictive control: current status and future directions. Computers and Chemical Engineering, 1998, 23(2): 187-202[13] Findeison R, Allgwer F, Biegler L T. Assessment and Future Directions of Nonlinear Model Predictive Control. Berlin: Springer, 2007[14] Magni L, Raimondo D M, Allgwer F. Nonlinear Model Predictive Control: Towards New Challenging Applications. Berlin: Springer-Verlag, 2009[15] Klatt K U, Marquadt W. Perspectives for process systems engineering —— personal views from academia and industry. Computers and Chemical Engineering, 2009, 33(3): 536-550[16] Froisy J B. Model predictive control —— building a bridge between theory and practice. Computers and Chemical Engineering, 2006, 30(10-12): 1426-1435[17] Xi Yu-Geng, Li De-Wei. Fundamental philosophy and status of qualitative synthesis model predictive control. Acta Automatica Sinica, 2008, 34(10): 1225-1234(席裕庚, 李德伟. 预测控制定性综合理论的基本思路和研究现状. 自动化学报, 2008, 34(10): 1225-1234)[18] Kothare M V, Balakrishnan V, Morari M. Robust constrained model predictive control using linear matrix inequalities. Automatica, 1996, 32(10): 1361-1379[19] Li Zhi-Jun, Liu Ji-Zhen, Tan Wen. Robust model predictive control for saturated systems. Control and Decision, 2006, 21(6): 641-645 (李志军, 刘吉臻, 谭文. 饱和约束系统的鲁棒模型预测控制. 控制与决策, 2006, 21(6): 641-645)[20] Chen Qiu-Xia, Yu Li. Robust model predictive control for uncertain discrete time-delay systems via dynamic output feedback. Control Theory and Applications, 2007, 24(3): 401-406(陈秋霞, 俞立. 不确定离散时滞系统的输出反馈鲁棒预测控制. 控制理论与应用, 2007, 24(3): 401-406)[21] Yu Shu-You, Chen Hong, Zhang Peng, Li Xue-Jun. An LMI optimization approach for enlarging the terminal region of MPC for nonlinear systems. Acta Automatica Sinica, 2008, 34(7): 798-804(于树友, 陈虹, 张鹏, 李学军. 一种基于LMI的非线性模型预测控制终端域优化方法. 自动化学报, 2008, 34(7): 798-804)[22] Wang Juan, Liu Zhi-Yuan, Chen Hong, Yu Shu-You, Pei Run. H∞ output feedback control of constrained systems via moving horizon strategy. Acta Automatica Sinica, 2007, 33(11): 1176-1181(王娟, 刘志远, 陈虹, 于树友, 裴润. 输入约束系统的滚动时域输出反馈控制方法研究. 自动化学报, 2007, 33(11): 1176-1181)[23] He De-Feng, Ji Hai-Bo, Zheng Tao. Nonlinear H∞ robust predictive control with bounded persistent disturbances. Acta Automatica Sinica, 2008, 34(2): 215-219(何德峰, 季海波, 郑涛. 持续有界扰动下的非线性H∞鲁棒预测控制. 自动化学报, 2008, 34(2): 215-219)[24] Liu Fei, Cai Yin. Constrained predictive control of Markov jump linear systems based on terminal invariant sets. Acta Automatica Sinica, 2008, 34(4): 496-499(刘飞, 蔡胤. 基于终端不变集的Markov跳变系统约束预测控制. 自动化学报, 2008, 34(4): 496-499)[25] Liu Xiao-Hua, Li Jing-Hui, Gao Rong, Wei Xin-Jiang. Based H∞ performance robust predictive control for uncertainty multirate system. Control and Decision, 2010, 25(9): 1287-1291, 1296(刘晓华, 李景会, 高荣, 魏新江. 基于H∞性能的不确定多速率系统鲁棒预测控制. 控制与决策, 2010, 25(9): 1287-1291, 1296)[26] He De-Feng, Xue Mei-Sheng, Ji Hai-Bo. Constructive model predictive control for constrained nonlinear systems. Control and Decision, 2008, 23(11): 1301-1304, 1310(何德峰, 薛美盛, 季海波. 约束非线性系统构造性模型预测控制. 控制与决策, 2008, 23(11): 1301-1304, 1310)[27] Liu Xiang-Jie, Liu Ji-Zhen. Constrained power plant coordinated predictive control using neurofuzzy model. Acta Automatica Sinica, 2006, 32(5): 785-790(刘向杰, 刘吉臻. 基于模糊神经模型的电厂协调预测控制. 自动化学报, 2006, 32(5): 785-790)[28] Zheng Yi, Li Shao-Yuan, Wang Xiao-Bo. Model-predictive-control for an accelerated cooling process. Control Theory and Applications, 2009, 26(7): 777-780(郑毅, 李少远, 王笑波. 加速冷却过程的模型预测控制. 控制理论与应用, 2009, 26(7): 777-780)[29] Chen Hong-Li, Lan Hai, Shen Yi. Study on generalized predictive control for π type rudder pitching stabilization. Journal of System Simulation, 2006, 18(12): 3508-3511(陈虹丽, 兰海, 沈毅. 基于π型舵船舶减纵摇广义预测控制研究. 系统仿真学报, 2006, 18(12): 3508-3511)[30] Sun Guang, Huo Wei. Direct-adaptive fuzzy predictive control of satellite attitude. Acta Automatica Sinica, 2010, 36(8): 1151-1159(孙光, 霍伟. 卫星姿态直接自适应模糊预测控制. 自动化学报, 2010, 36(8): 1151-1159)[31] Zhang Jin-Zhao, Liu Guo-Hai, Pan Tian-Hong. Multi-model predictive control for multi-motor variable frequency speed-regulating synchronous system. Control and Decision, 2009, 24(10): 1489-1494(张今朝, 刘国海, 潘天红. 多电机变频调速同步系统的多模型预测控制. 控制与决策, 2009, 24(10): 1489-1494)[32] Fu Hui, Hu Gang, Xu Jian-Min, Xu Lun-Hui. Traffic flow predictive control method of urban correlation intersection based on neural network. China Journal of Highway and Transport, 2008, 21(5): 91-95(傅惠, 胡刚, 徐建闽, 许伦辉. 基于神经网络的城市关联交叉口交通流预测控制方法. 中国公路学报, 2008, 21(5): 91-95)[33] Cui Wei, Wang Chang-De, Guan Guang-Hua, Fan Jie. Model predictive control for automatic operation of canals. Journal of Hydraulic Engineering, 2005, 36(8): 1000-1006(崔巍, 王长德, 管光华, 范杰. 渠道运行管理自动化的多渠段模型预测控制. 水利学报, 2005, 36(8): 1000-1006)[34] Wang Zi-Yang, Qin Lin-Lin, Wu Gang, Lv Xu-Tao. Modeling of greenhouse temperature-humid system and model predictive control based on switching system control. Transactions of the Chinese Society of Agricultural Engineering, 2008, 24(7): 188-192(王子洋, 秦琳琳, 吴刚, 吕旭涛. 基于切换控制的温室温湿度控制系统建模与预测控制. 农业工程学报, 2008, 24(7): 188-192)[35] Lu J Z. Challenging control problems and emerging technologies in enterprise optimization. Control Engineering Practice, 2003, 11(8): 847-858[36] Bellemans T, De Schutter B, De Moor B. Model predictive control for ramp metering of motorway traffic: A case study. Control Engineering Practice, 2006, 14(7): 757-767[37] van Overloop P J, Weijs S, Dijkstra S. Multiple model predictive control on a drainage canal system. Control Engineering Practice, 2008, 16(5): 531-540[38] G{\'omez M, Rodellar J, Mantec{\'on J A. Predictive control method for decentralized operation of irrigation canals. Applied Mathematical Modelling, 2002, 26(11): 1039-1056[39] Scattolini, R. Architectures for distributed and hierarchical model predictive control —— a review. Journal of Process Control, 2009, 19(5): 723-731[40] Du X N, Xi Y G, Li S Y. Distributed model predictive control for large-scale systems. In: Proceedings of the 2001 American Control Conference. Arlington, VA: IEEE, 2001. 3142-3143[41] Camponogara E, Jia D, Krogh B H, Talukdar S. Distributed model predictive control. IEEE Control Systems Magazine, 2002, 22(1): 44-52[42] Stewart B T, Venkat A N, Rawlings J B, Wright S J, Pannocchia G. Cooperative distributed model predictive control. System and Control Letters, 2010, 59(8): 460-469[43] Zheng Y, Li S Y, Wang X B. Distributed model predictive control for plant-wide hot-rolled strip laminar cooling process. Journal of Process Control, 2009, 19(9): 1427-1437[44] Venkat A N, Hiskens I A, Rawlings J B, Wright S J. Distributed MPC strategies with application to power system automatic generation control. IEEE Transactions on Control Systems Technology, 2008, 16(6): 1192-1206[45] Negenborn R R, De Schutter B, Hellendoorn H. Multi-agent model predictive control of transportation networks. In: Proceedings of the 2006 IEEE International Conference on Networking, Sensing and Control. Ft. Lauderdale, FL: IEEE, 2006. 296-301[46] Dunbar W B, Murray R M. Distributed receding horizon control for multi-vehicle formation stabilization. Automatica, 2006, 42(4): 549-558[47] Wan Z Y, Kothare M V. An efficient off-line formulation of robust model predictive control using linear matrix inequalities. Automatica, 2003, 39(5): 837-846[48] Ding B C, Xi Y G, Cychowski M T, O'Mahony T. Improving off-line approach to robust MPC based-on nominal performance cost. Automatica, 2007, 43(1): 158-163[49] Angeli D, Casavola A, Franz{é G, Mosca E. An ellipsoidal off-line MPC scheme for uncertain polytopic discrete-time systems. Automatica, 2008, 44(12): 3113-3119[50] Canale M, Fagiano L, Milanese M. Set membership approximation theory for fast implementation of model predictive control laws. Automatica, 2009, 45(1): 45-54[51] Bemporad A, Morari M, Dua V, Pistikopoulos E N. The explicit linear quadratic regulator for constrained systems. Automatica, 2002, 38(1): 3-20[52] Grancharova A, Johansen T A. Survey of explicit approaches to constrained optimal control. Switching and Learning in Feedback Systems. Berlin: Springer-Verlag, 2005. 47-97[53] Grancharova A, Johansen T A. Explicit approximate model predictive control of constrained nonlinear systems with quantized input. Nonlinear Model Predictive Control. Berlin: Springer-Verlag, 2009. 371-380[54] Due P, Kouramas K, Dua V, Pistikopoulos E N. MPC on a chip —— recent advances on the application of multi-parametric model-based control. Computers and Chemical Engineering, 2008, 32(4-5): 754-765[55] Kouvaritakis B, Cannon M, Rossiter J A. Who needs QP for linear MPC anyway? Automatica, 2002, 38(5): 879-884[56] Wen C T, Ma X Y, Ydstie B E. Analytical expression of explicit MPC solution via lattice piecewise-affine function. Automatica, 2009, 45(4): 910-917[57] Borrelia F, Baotic M, Pekar J, Stewart G. On the computation of linear model predictive control laws. Automatica, 2010, 46(6): 1035-1041[58] Kouramas K I, Faísca N P, Panos C, Pistikopoulos E N. Explicit/multi-parametric model predictive control (MPC) of linear discrete-time systems by dynamic and multi-parametric programming. Automatica, 2011, 47(8): 1638-1645[59] Grancharova A, Johansen T A. Computation, approximation and stability of explicit feedback min-max nonlinear model predictive control. Automatica, 2009, 45(5): 1134-1143[60] Ricker N L. Use of quadratic programming for constrained internal model control. Industrial and Engineering Chemistry Process Design and Development, 1985, 24(4): 925-936[61] Richalet J, Abu El Ata-Doss S, Arber C, Kuntze H B, Jacubasch A, Schill W. Predictive functional control. In: Proceedings of the 10th IFAC World Congress. Munich, Germany: 1987, (4): 261-268[62] Cagienard R, Grieder P, Kerrigan E C, Morari M. Move blocking strategies in receding horizon control. Journal of Process Control, 2007, 17(6): 563-570[63] Gondhalekar R, Imura J, Kashima K. Controlled invariant feasibility —— a general approach to enforcing strong feasibility in MPC applied to move-blocking. Automatica, 2009, 45(12): 2869-2875[64] Gondhalekar R, Imura J. Least-restrictive move-blocking model predictive control. Automatica, 2010, 46(7): 1234-1240[65] Li D W, Xi Y G. The general framework of aggregation strategy in model predictive control and stability analysis. In: Proceedings of the 11th IFAC Symposium on Large Scale Systems Theory and Applications. Gdansk, Poland: IFAC, 2007. 192-197[66] Li D W, Xi Y G. Quality guaranteed aggregation based model predictive control and stability analysis. Science in China Series F: Information Sciences, 2009, 52(7): 1145-1156[67] Wang Y, Boyd S. Fast model predictive control using online optimization. In: Proceedings of the 17th IFAC World Congress. Seoul, Korea: IFAC, 2008. 6974-6979[68] Diehl M, Bock H G, Schl{er J P, Findeisen R, Nagy Z, Allg{er F. Real-time optimization and nonlinear model predictive control of processes governed by differential-algebraic equations. Journal of Process Control, 2002, 12(4): 577-585[69] Wirsching L, Albersmeyer J, Kühl P, Diehl M, Bock G. An adjoint-based numerical method for fast nonlinear model predictive control. In: Proceedings of the 17th IFAC World Congress. Seoul, Korea: IFAC, 2008. 1934-1939[70] Pannocchia G, Rawlings J B, Wright S J. Fast, large-scale model predictive control by partial enumeration. Automatica, 2007, 43(5): 852-860[71] Liu S B, Wang J. A simplified dual neural network for quadratic programming with its KWTA application. IEEE Transactions on Neural Networks, 2006, 17(6): 1500-1510[72] Mayne D Q, Rakovi#263; S V, Findeisen R, Allg{wer F. Robust output feedback model predictive control of constrained linear systems: time varying case. Automatica, 2009, 45(9): 2082-2087[73] L{óvaas C, Seron M M, Goodwin G C. Robust output-feedback model predictive control for systems with unstructured uncertainty. Automatica, 2008, 44(8): 1933-1943[74] Ding B C, Xi Y G, Cychowski M T, O'Mahony T. A synthesis approach for output feedback robust constrained model predictive control. Automatica, 2008, 44(1): 258-264[75] Ding B. Constrained robust model predictive control via parameter-dependent dynamic output feedback. Automatica, 2010, 46(9): 1517-1523[76] Park J H, Kim T H, Sugie T. Output feedback model predictive control for LPV systems based on quasi-min-max algorithm. Automatica, 2011, 47(9): 2052-2058[77] Kouvaritakis B, Rossiter J A, Schuurmans J. Efficient robust predictive control. IEEE Transactions on Automatic Control, 2000, 45(8): 1545-1549[78] Cannon M, Kouvaritakis B, Lee Y I, Brooms A C. Efficient non-linear model based predictive control. International Journal of Control, 2001, 74(4): 361-372[79] Imsland L, Bar N, Foss B A. More efficient predictive control. Automatica, 2005, 41(8): 1395-1403[80] Lee Y I, Kouvaritakis B. Constrained robust model predictive control based on periodic invariance. Automatica, 2006, 42(12): 2175-2181[81] Li D W, Xi Y G, Zheng P Y. Constrained robust feedback model predictive control for uncertain systems with polytopic description. International Journal of Control, 2009, 82(7): 1267-1274[82] Li D W, Xi Y G. Design of robust model predictive control based on multi-step control set. Acta Automatica Sinica, 2009, 35(4): 433-437[83] Li D W, Xi Y G. The feedback robust MPC for LPV systems with bounded rates of parameter changes. IEEE Transactions on Automatic Control, 2010, 55(2): 503-507[84] Lee S M, Park J H, Ji D H, Won S C. Robust model predictive control for LPV systems using relaxation matrices. IET Control Theory and Applications, 2007, 1(6): 1567-1573 [85] Lee Y II, Kouvaritakis B. Robust receding horizon predictive control for systems with uncertain dynamics and input saturation. Automatica, 2000, 36(10): 1497-1504[86] Pluymers B, Rossiter J A, Suykens J A K, De Moor B. The efficient computation of polyhedral invariant sets for linear systems with polytopic uncertainty. In: Proceedings of the 2005 American Control Conference. Portland, USA: IEEE, 2005. 804-809[87] Alessio A, Lazar M, Bemporad A, Heemels W P M H. Squaring the circle: an algorithm for generating polyhedral invariant sets from ellipsoidal ones. Automatica, 2007, 43(12): 2096-2103[88] Huang H, Li D W, Lin Z L, Xi Y G. An improved robust model predictive control design in the presence of actuator saturation. Automatica, 2011, 47(4): 861-864[89] Chen H. A feasible moving horizon H∞ control scheme for constrained uncertain linear systems. IEEE Transactions on Automatic Control, 2007, 52(2): 343-348[90] Allgwer F, Findeisen R, Nagy Z K. Nonlinear model predictive control: from theory to application. Journal of the Chinese Institute of Chemical Engineers, 2004, 35(3): 299-315[91] Ding B C. Dynamic output feedback predictive control for nonlinear systems represented by a Takagi-Sugeno model. IEEE Transactions on Fuzzy Systems, 2011, 19(5): 831-843[92] Zhang T J, Feng G, Zeng X J. Output tracking of constrained nonlinear processes with offset-free input-to-state stable fuzzy predictive control. Automatica, 2009, 45(4): 900-909[93] Zhang T J, Feng G, Lu J H. Fuzzy constrained min-max model predictive control based on piecewise Lyapunov functions. IEEE Transactions on Fuzzy Systems, 2007, 15(4): 686-698[94] Xia Y Q, Yang H J, Shi P, Fu M Y. Constrained infinite-horizon model predictive control for fuzzy-discrete-time systems. IEEE Transactions on Fuzzy Systems, 2010, 18(2): 429-436[95] Bemporad A, Morari M. Control of systems integrating logic, dynamics, and constraints. Automatica, 1999, 35(3): 407-427[96] Lazar M, Heemels W P M H. Predictive control of hybrid systems: input-to-state stability results for sub-optimal solutions. Automatica, 2009, 45(1): 180-185[97] Bemporad A, Di Cairano S. Model-predictive control of discrete hybrid stochastic automata. IEEE Transactions on Automatic Control, 2011, 56(6): 1307-1321[98] Falugi P, Olaru S, Dumur D. Multi-model predictive control based on LMI: from the adaptation of the state-space model to the analytic description of the control law. International Journal of Control, 2010, 83(8): 1548-1563[99] García M R, Vilas C, Santos L O, Alonso A A. A robust multi-model predictive controller for distributed parameter systems. Journal of Process Control, 2012, 22(1): 60-71[100] Cannon M, Kouvaritakis B, Wu X J. Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints. Automatica, 2009, 45(1): 167-172[101] Su Y, Tan K K, Lee T H. Comments on "Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints". Automatica, 2011, 47(2): 427-428[102] Kouvaritakis B, Cannon M, Rakovi#263; S V, Cheng Q F. Explicit use of probabilistic distributions in linear predictive control. Automatica, 2010, 46(10): 1719-1724[103] Cannon M, Kouvaritakis B, Wu X J. Probabilistic constrained MPC for multiplicative and additive stochastic uncertainty. IEEE Transactions on Automatic Control, 2009, 54(7): 1626-1632[104] Cannon M, Kouvaritakis B, Rakovi#263; S V, Cheng Q F. Stochastic tubes in model predictive control with probabilistic constraints. IEEE Transactions on Automatic Control, 2011, 56(1): 194-200[105] Bernardini D, Bemporad A. Stabilizing model predictive control of stochastic constrained linear systems. IEEE Transactions on Automatic Control, 2012, 57(6): 1468-1480[106] Wen J W, Liu F. Receding horizon control for constrained Markovian jump linear systems with bounded disturbance. Journal of Dynamic Systems, Measurement, and Control, 2011, 133(1): 011005[107] Wang C, Ong C J, Sim M. Convergence properties of constrained linear system under MPC control law using affine disturbance feedback. Automatica, 2009, 45(7): 1715-1720[108] Wang C, Ong C J, Sim M. Constrained linear system with disturbance: convergence under disturbance feedback. Automatica, 2008, 44(10): 2583-2587[109] Wang C, Ong C J, Sim M. Model predictive control using segregated disturbance feedback. IEEE Transactions on Automatic Control, 2010, 55(4): 831-840[110] 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[111] Rawlings J B, Amrit R. Optimizing process economic performance using model predictive control. Nonlinear Model Predictive Control. Berlin: Springer-Verlag, 2009. 119-138[112] Adetola V, Guay M. Integration of real-time optimization and model predictive control. Journal of Process Control, 2010, 20(2): 125-133[113] Gunnerud V, Foss B, Torgnes E. Parallel Dantzig-Wolfe decomposition for real-time optimization-applied to a complex oil field. Journal of Process Control, 2010, 20(9): 1019-1026[114] Shead L R E, Muske K R, Rossiter J A. Conditions for which linear MPC converges to the correct target. Journal of Process Control, 2010, 20(10): 1243-1251[115] Xu Z H, Zhu Y C, Han K, Zhao J, Qian J X. A multi-iteration pseudo-linear regression method and an adaptive disturbance model for MPC. Journal of Process Control, 2010, 20(4): 384-395[116] Gruber J K, Ramirez D R, Alamo T, Bordons C, Camacho E F. Control of a pilot plant using QP based min-max predictive control. Control Engineering Practice, 2009, 17(11): 1358-1366[117] Alamo T, Ramirez D R, de la Peńa, D M, Camacho E F. Min-max MPC using a tractable QP problem. Automatica, 2007, 43(4): 693-700[118] Tsai C C, Lin S C, Wang T Y, Teng F J. Stochastic model reference predictive temperature control with integral action for an industrial oil-cooling process. Control Engineering Practice, 2009, 17(2): 302-310[119] Grancharova A, Kocijan J, Johansen T A. Explicit stochastic predictive control of combustion plants based on gaussian process models. Automatica, 2008, 44(6): 1621-1631[120] Peng H, Wu J, Inoussa G, Deng Q L, Nakano K. Nonlinear system modeling and predictive control using the RBF nets-based quasi-linear ARX model. Control Engineering Practice, 2009, 17(1): 59-66[121] Cueli J R, Bordons C. Iterative nonlinear model predictive control. Stability, robustness and applications. Control Engineering Practice, 2008, 16(9): 1023-1034[122] Wang Y Q, Zhou D H, Gao F R. Iterative learning model predictive control for multi-phase batch processes. Journal of Process Control, 2008, 18(6): 543-557[123] Cao R Z, Low K S. A repetitive model predictive control approach for precision tracking of a linear motion system. IEEE Transactions on Industrial Electronics, 2009, 56(6): 1955-1962[124] STREP Project 223854: Hierarchical and Distributed Model Predictive Control of Large Scale Systems [Online], available: ftp://ftp.cordis.europa.eu/pub/fp7/ict/docs/ necs/20081020-12-hd-mpc_en.pdf, February 28, 2013[125] Ocampo-Martinez C, Barcelli D, Puig V, Bemporad A. Hierarchical and decentralised model predictive control of drinking water networks: application to Barcelona case study. IET Control Theory and Applications, 2012, 6(1): 62-71[126] Piotrowski R, Brdys M A, Konarczak K, Duzinkiewicz K, Chotkowski W. Hierarchical dissolved oxygen control for activated sludge processes. Control Engineering Practice, 2008, 16(1): 114-131[127] Edlund K, Bendtsen J D, Jorgensen J B. Hierarchical model-based predictive control of a power plant portfolio. Control Engineering Practice, 2011, 19(10): 1126-1136[128] Zheng Y, Li S Y, Li N. Distributed model predictive control over network information exchange for large-scale systems. Control Engineering Practice, 2011, 19(7): 757-769[129] Lin S, De Schutter B, Xi Y G, Hellendoorn H. Fast model predictive control for urban road networks via MILP. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(3): 846-856[130] Ghods A H, Fu L P, Rahimi-Kian A. An efficient optimization approach to real-time coordinated and integrated freeway traffic control. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(4): 873-884[131] Zhang Y, Li S Y. Networked model predictive control based on neighbourhood optimization for serially connected large-scale processes. Journal of Process Control, 2007, 17(1): 37-50[132] Munoz De La Pena D, Christofides P D. Lyapunov-based model predictive control of nonlinear systems subject to data losses. IEEE Transactions on Automatic Control, 2008, 53(9): 2076-2089[133] Liu G P, Xia Y Q, Chen J, Rees D, Hu W. Networked predictive control of systems with random network delays in both forward and feedback channels. IEEE Transactions on Industrial Electronics, 2007, 54(3): 1282-1297[134] Gruber J K, Doll M, Bordons C. Design and experimental validation of a constrained MPC for the air feed of a fuel cell. Control Engineering Practice, 2009, 17(8): 874-885[135] Garcia-Gabin W, Zambrano D, Camacho E F. Sliding mode predictive control of a solar air conditioning plant. Control Engineering Practice, 2009, 17(6): 652-663[136] Roca L, Guzman J L, Normey-Rico J E, Berenguel M, Yebra L. Robust constrained predictive feedback linearization controller in a solar desalination plant collector field. Control Engineering Practice, 2009, 17(9): 1076-1088[137] Kusiak A, Song Z, Zheng H Y. Anticipatory control of wind turbines with data-driven predictive models. IEEE Transactions on Energy Conversion, 2009, 24(3): 766-774[138] Teleke S, Baran M E, Bhattacharya S, Huang A Q. Optimal control of battery energy storage for wind farm dispatching. IEEE Transactions on Energy Conversion, 2010, 25(3): 787-794[139] Xia X H, Zhang J F, Elaiw A. An application of model predictive control to the dynamic economic dispatch of power generation. Control Engineering Practice, 2011, 19(6): 638-648[140] Perez T, Goodwin G C. Constrained predictive control of ship fin stabilizers to prevent dynamic stall. Control Engineering Practice, 2008, 16(4): 482-494[141] Li Z, Sun J. Disturbance compensating model predictive control with application to ship heading control. IEEE Transactions on Control Systems Technology, 2012, 20(1): 257-265[142] Coronaand D, De Schutter B. Adaptive cruise control for a smart car: a comparison benchmark for MPC-PWA control methods. IEEE Transactions on Control Systems Technology, 2008, 16(2): 365-372[143] Asadi B, Vahidi A. Predictive cruise control: utilizing upcoming traffic signal information for improving fuel economy and reducing trip time. IEEE Transactions on Control Systems Technology, 2011, 19(3): 707-714[144] Li S B, Li K Q, Rajamani R, Wang J Q. Model predictive multi-objective vehicular adaptive cruise control. IEEE Transactions on Control Systems Technology, 2011, 19(3): 556-566[145] Di Cairano S, Ynankiev D, Bemporad A, Kolmanovsky I V, Hrovat D. Model predictive idle speed control: design, analysis, and experimental evaluation. IEEE Transactions on Control Systems Technology, 2012, 20(1): 84-97[146] Yoon Y, Shin J, Kim H J, Park Y, Sastry S. Model-predictive active steering and obstacle avoidance for autonomous ground vehicles. Control Engineering Practice, 2009, 17(7): 741-750[147] Geyer T, Papafotiou G, Morari M. Model predictive direct torque control —— part I: concept, algorithm and analysis. IEEE Transactions on Industrial Electronics, 2009, 56(6): 1894-1905[148] Preindl M, Schaltz E, Thogersen P. Switching frequency reduction using model predictive direct current control for high-power voltage source inverters. IEEE Transactions on Industrial Electronics, 2011, 58(7): 2826-2835[149] Bolognani S, Peretti L, Zigliotto M. Design and implementation of model predictive control for electrical motor drives. IEEE Transactions on Industrial Electronics, 2009, 56(6): 1925-1936[150] Beccuti A G, Mariethoz S, Cliquennois S, Wang S, Morari M. Explicit model predictive control of dc-dc switched-mode power supplies with extended Kalman filtering. IEEE Transactions on Industrial Electronics, 2009, 56(6): 1864-1874[151] Cychowski M, Szabat K, Orlowska-Kowalska T. Constrained model predictive control of the drive system with mechanical elasticity. IEEE Transactions on Industrial Electronics, 2009, 56(6): 1963-1973[152] Xi X C, Poo A N, Chou S K. Support vector regression model predictive control on a HVAC plant. Control Engineering Practice, 2007, 15(8): 897-9080[153] Alexis K, Nikolakopoulos G, Tzes A. Switching model predictive attitude control for a quadrotor helicopter subject to atmospheric disturbances. Control Engineering Practice, 2011, 19(10): 1195-1207[154] Gavilan F, Vazquez R, Camacho E F. Chance-constrained model predictive control for spacecraft rendezvous with disturbance estimation. Control Engineering Practice, 2012, 20(2): 111-122[155] From P J, Gravdahl J T, Lillehagen T, Abbeel P. Motion planning and control of robotic manipulators on seaborne platforms. Control Engineering Practice, 2011, 19(8): 809-819[156] Percival M W, Wang Y, Grosman B, Dassau E, Zisser H, Jovanovic L, Doyle F J III. Development of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters. Journal of Process Control, 2011, 21(3): 391-404[157] Blanco E, De Prada C, Cristea S, Casas J. Nonlinear predictive control in the LHC accelerator. Control Engineering Practice, 2009, 17(10): 1136-1147[158] Valencia-Palomo G, Rossiter J A. Efficient suboptimal parametric solutions to predictive control for PLC applications. Control Engineering Practice, 2011, 19(7): 732-743[159] Vouzis P D, Kothare M V, Bleris L G, Arnold M G. A system-on-a-chip implementation for embedded real-time model predictive control. IEEE Transactions on Control Systems Technology, 2009, 17(5): 1006-1017[160] Lau M S K, Yue S P, Ling K V, Maciejowski J M. A comparison of interior point and active set methods for FPGA implementation of model predictive control. In: Proceedings of the 2009 European Control Conference. Budapest, Hungary: EUCA, 2009. 156-160[161] Wills A G, Knagge G, Ninness B. Fast linear model predictive control via custom integrated circuit architecture. IEEE Transactions on Control Systems Technology, 2012, 20(1): 59-71[162]Yang N, Li D W, Zhang J, Xi Y G. Model predictive control system based on FPGA and a case study. In: Proceedings of the 2011 IFAC World Congress. Milan, Italy: IFAC, 2011. 9266-9271[163]Li D W, Yang N, Niu R, Qiu H, Xi Y G. FPGA based QDMC control for reverse-osmosis water desalination system. Desalination, 2012, 285: 83-90
点击查看大图
计量
- 文章访问数: 10478
- HTML全文浏览量: 642
- PDF下载量: 14233
- 被引次数: 0