摘要
预测控制是面向实际工业过程发展起来的一类先进控制方法，一直深受控制界的关注，将智能控制理论与预测控制机理相结合，使预测控制向智能化方向发展，以满足复杂工业过程控制的需要，是当前预测控制发展的新趋势。本文针对工业控制过程中的实际问题对智能预测控制进行了深入的研究，提出了几种新的智能预测控制算法，并结合浙江省重点科技项目，以水泥生产过程中关键性工艺环节为对象，设计了一类新的智能预测控制器。实际运行结果表明，该算法能获得良好的控制效果。总结全文，论文的主要内容可概括如下：
1．在介绍了T-S模糊模型和GPC基本原理的基础上，将基于T-S模型的GPC归纳为三种算法，从理论上对这三种算法进行了详细地推导，并通过仿真研究比较了三种算法的控制性能和计算负担上的差异；从而为这一类模糊预测控制的实际应用提供了选择的依据，也为进一步的性能分析奠定了基础。
2．根据水泥生产线系统复杂、干扰频繁、全局模型建立困难的实际情况，通过对回转窑烧成工艺和分解炉过程特性的分析，提出一种基于T-S模型的多模结构的预测控制算法，并在DCS系统中开发了实时控制的软件。实际应用表明，对于存在着大时滞和参数时变的水泥回转窑系统，该算法不但能获得良好的控制精度(温度波动不超过850±10％℃)，而且能明显地提高水泥熟料的分解率。
3．灰色预测是根据被控对象的动态行为和运行趋势进行分析的预测方式。它具有建模数据量小，计算简单的特点，适用于不确定性复杂系统动态预测。在介绍了灰色系统建模理论的基础上，将灰色模型引入到预测模糊控制中。针对灰色预测步长对控制性能影响进行了详细分析，在此基础上，首次提出一种基于预测步长自调整的灰色预测模糊控制器。通过仿真研究验证了该方法能明显地改善常规模糊控制和固定预测步长的灰色预测模糊控制效果。
4．神经网络预测控制是智能预测控制的一个重要组成部分。通过对PH中和过程的分析，针对常规DMC方法在控制该过程中存在的问题，提出一种基于CMAC的非线性DMC控制算法，并通过仿真验证了该算法能有效地克服常规
浙门刁、学协l学位论义 仰I儿
D＊C方沿存在的不足，有效地改故了控十帅l能。
。5．实！讨性和收敛性的矛盾是N祈￥遗传算法在非线性预测控制滚动优化叶。应
用的一大戒题，针对这一问题，本文对标准迅传算法进行了改进，首次提山一fZ。
基于启发式遗传算法。根扼被控对象的状态信；③、，在实时优化中适时调整控制显
的搜索区域，并将这种方法应用于基于＊＊＊C网络的非线性预测控制中。仿真
结果表明，该优化方法能较好地克服了标准遗传算法所存在的缺陷，不仅实时性
较强，控制精度也有显著提高，从而为提升遗传算法在非线性预测控制中的适用
性作出了有益的探索。
Intelligent predictive control has started to present as one of the new research domain of model predictive control. The studies and applications of intelligent predictive is discussed in this dissertation. Several new control strategies are present in this paper. In this paper, a kind of intelligent predictive control algorithm is designed and applied in Hangzhou cement plant. In conclusion the main contents are as follows:
1. Based on the introduction of the principles of Takagi-Sugeno(T-S) fuzzy model and generalized predictive control(GPC) algorithm, the fuzzy predictive control method combining GPC and T-S model is classified as three kinds of algorithms. The design method of these algorithms is presented in detail. A comparison of these FGPC strategies in control performance and complexity of computation is given by simulation.
2. The cement rotary kiln is a complex process, which has nonlinear and time-varying characteristic and exist strong and frequent disturbance, so global model is hard to establish. Based on analysis of the characteristics of the decomposing furnace in Hangzhou cement rotary kiln plant, a new fuzzy predictive control method with multi-model structure is proposed. The parameters of T-S fuzzy model can be modified, depending on the flow of raw material. Real time control software of this algorithm has been developed on the platform of Plantscape distributed control system of Honeywell Corporation. Application results exhibit the superiority of the proposed method over conventional ones even on the condition of existing large delay and time-varying parameters. It has improved the decomposing rate of cement from 81.5% to 89.1%, ensured the temperature varying within ?0% 癈.
3.Grey prediction is a kind of effective prediction method, which only needs few output sample data (four data are enough) for modeling a system. Therefore, it is easy to model a system without complex computations. Based on the introduction of grey system modeling theory, a kind of fuzzy predictive controller using grey model is
in
proposed. A fuzzy decision making mechanism of the grey model 's prediction step is applied to improve the performance of fuzzy controller. Simulation results show the superiority of the proposed method over the conventional fuzzy controller and the grey model predictive fuzzy controller with fix predictive step size.
4. Neural net predictive control is an important branch of the intelligent predictive method. By considering the nonlinear characteristic of PH neutralization process, a nonlinear DMC control scheme, based on the CM AC model which is used to model the titration equation, is proposed to overcome the nonlinear disturbance caused by the unknown spices existing in the process stream. The effectiveness of this strategy has been verified via simulation.
5. The conflict between convergent speed and solution accuracy has been the impediment when GA is applied in nonlinear predictive control. In order to overcome this problem, a heuristic generic algorithm (HGA) is proposed in nonlinear predictive controller based on CMAC predictive model. The main characteristics of HGA are that it can change search space online according to the process state while performing the online optimization. So it can enhance the convergent speed and solution accuracy simultaneously. Simulation results demonstrate the feasibility of the proposed algorithm.
引文
[1] Richalet J. and Rault A.(1978). Model predictive heuristic control: Application to Industrial Process. Automatica, 14(1): 413～428
[2] Rouhani R. and Mehra R.K., Model Algorithmic Control(MAC): Basic Theorytical Properties, Automatica, 1982,18(4):401～414
[3] Culter C.R. and Ramaker B.L., Dynamic Matrix Control-A Computer Control Algorithm, proc. JACC, San Fancisco,WP5-B 1980
[4] Clarke D.W., et. al. Generalized Predictive Control-Part 1.Basic Algorithm, Automatica, 1987, 23(2): 137-148
[5] Clarke D.W., et. al. Generalized Predictive Control-Part 2.Extentions and Interpretations, Automatica, 1987,23(2): 149-160
[6] Lelic M.A. and Zarrop M. B.(1987). Generalized Pole-placement Self-tunning Controller, Part 1 and Part 2, Int. J. Control, 46(2): 547～601
[7] Ydstie B. E., el. al. Theory and Application of an extended horizon self-tunning controller, AIChE J., 1985, 31(11): 1771～1780
[8] Bruijin P. M., el. al. Predictive control using impulse response models. 6th
IFAC/IFIP Conf.,Dusseldof, 1980
[9] Zheng Qinbao, el. al. Intelligent self-tunning model algorithmic control, proc. Intern. Conf., Signals and Syst., Dalian, Tassin: AMSE Press, 1989
[10] 袁璞，左信，单值预估控制的稳定性。首都高校首届自动控制学会报告论文集，清华大学，1990
[11] 舒迪前，自适应控制，东北大学出版社，1993
[12] 石中锁，舒迪前．一种前馈校正极点配置自适应动态矩阵控制算法，计算技术与自动化，1993，12(3)：15～18
[13] 石中锁，舒迪前．隐式自校正动态矩阵控制器的全局收敛性，北京科技大学学报，1994,16(4)
[14] 李伟等，双值DMC控制及在快速动态过程中的应用，计算机技术与自动化，1993，12(1)：20～23
[15] 张青，比例—双信号动态DMC．清华大学学报：自然版，1998,38(5)：69～71
[16] 席裕庚，李彤，广义动态矩阵—PID串级控制，俞金寿等编：中国自动化学会第二届过程控制科学报告会论文集，上海：华东化工学院出版社，1989，145～151
[17] 彭辉，具有PI结构的改进自校正DMC加权控制算法，电气自动化，1997，19(4)：41～44
[18] 杨马英，小波变换和主元分析在DMC算法中的应用，信息与控制，1997，26(6)：420～426
[19] 王伟，广义预测自适应控制的直接方法及全局收敛性分析，自动化学报，1995，21(1)：57～62
[20] 王伟，一种广义预测自适应控制的直接方法，自动化学报，1996，22(3)：270～276
[21] 袁著祉，递推广义预测自适应控制器，自动化学报，1989，15(4)：348-351
[22] Yamamoto T., el. al. A design method of self-tunning PID controllers, proc. of ACC, Maryland, USA, 1994, pp: 3263～3267
[23] Corradini M.L. and Orlando G. A VSC algorithm based on generalized predictive control, Automatica, 1997, 33(4): 927～932
[24] Ordys A.W. and Clarke D.W. A state-space description for GPC controllers. Int. J. Systems, 1993, 24(9): 1727～1744
[25] 杨建军等，基于状态空间的稳定广义预测控制器，控制与决策，1998，13(增刊)：475～478
[26] 赵怀彬，王诗苾，快速递推广义预测控制，控制理论及应用，1998，15(2)：190～196
[27] 幕德俊、佟明安，基于输入输出模型广义预测的并行算法，控制理论与应用，1997，14(1)：80～84
[28] 王轶，席裕庚，广义预测控制的并行算法．自动化学报，1996，22(1)：74～78
[29] 邹志云，顾钟文，周春晖，新型自适应模型算法控制算式，控制与决策，1988，3(3)：11～16
[30] 贾磊，孙优贤，增益自适应内模控制器及其在纸机控制中的应用，信息与控制，1993，22(4)：242～245
[31] 袁著祉，崔保民，新型随机广义预测控制自校正控制器，自动化学报，1992，18(3)：282～289
[32] 王倩，荣德善，单变量隐式广义控制自校正算法，计算机技术与自动化，1990，9(3)：1～5
[33] Camacho E. F. and Bordons C., Implementation of self-tuning generalize predictive controllers for the process industry, Int. J. Adapt. Control Signal Process, t993, 7: 63～73
[34] Morningred D.J., et. al., An adaptive nonlinear predictive controller, Chem. Eng. Sci., 1992,47(4): 755～762
[35] Lee J., el. al., Adaptive model predictive control using ARMA Models, proc. Asian Control Conference, Tokyo, 1994: 505～508
[36] Youssef C.B., et. al. Multi adaptive predictive control of an aerated lagoon for a wastewater treatment process, J. of Process Control, 1996, 6(5): 265～275
[37] 彭辉，具有比例积分结构的模型算法自适应控制器，中南大学学报，1999，Vol．30(2)：209～211
[38] 孙韬，游少鹏，一种多变量系统广义自适应控制的直接方法，四川联合大学
学报，1999，Vol．3(2)：96～102
[39] 汪锐，汪仁煌，蒸气炉温度的一种自适应预测控制，广东工业大学学报，2001，Vol．18(2)：31～35
[40] J. Richalet, et. al. Predictive functional control: Application to fast and accurate robots. Proc. of Automatic Control in 10th Triennial World Congress of IFAC, Munich, 1988: 251～258
[41] Richalet J., Industrial Application of Model Based Predictive Control. Automatica, 1993,29(5):1251～1274
[42] M.McDonnell and S.A. Ata-Doss Predictive Functional of Multivarible System with More Outputs than Inputs. proc.28th CDC 1989,Tampa,FL,USA: 1591～1596
[43] S.A Ata-Doss., et. at., Handling input and state constraits in predictive functional control, proc. 30th CDC, Brighton, UK,1991(12): 985～990
[44] Richalet J., Parametric predictive control of enthalpic processes, ADERSA, 1996
[45] 金晓明，模糊控制、预测控制与工业过程的先进控制，浙江大学博士学位论文，1998
[46] 赵昀，荣冈，王树青，预测函数控制PFC的稳定性及鲁棒性分析，中国控制会议论文集，1998(9)：327～332
[47] 楼史进，王骥程，王树青，精馏塔塔釜的热焓参数预测控制，1998中国控制与决策学术年会论文集，1998：168～171
[48] 张泉灵，预测函数控制及其应用研究，化工自动化及仪表，1998，25(增刊)
[49] 潘红华，苏宏业等，基于二阶Volterra模型的预测函数控制方法的研究，信息与控制，1999，28(增刊)：98～101
[50] 张新民，荣冈，间歇反应过程的预测函数控制，信息与控制，1999，28(增刊)：102～106
[51] C.E.Garcia, Quadratic Dynamic Matrix Control of Nonlinear Process.An Application to a Batch Reactor Processes, AIChE Annual Meeting, San Francisco, 1984
[52] J.H. Lee and N.L.k Ricker Extended kalman filter based nonlinear model predictve control, Ind. Eng. Chem. Res.,1994, 33: 1530～1541
[53] 刘兵，冯纯伯，李长庚，具有间隙预补偿的非线性预测控制．控制理论与应用，1998，16(2)：241～243
[54] R. Maner. Bryon, et. al., Nonlinear model predictive control of a simulated multivariable polymerization reactor using second-order volterra models. Automatica, 1996, 32(9): 1285～1301
[55] 黄河清，黄道，俞金寿，一类非线性系统的模型算法控制的研究，中国自动化学会首届过程控制科学报告会议论文集，浙江大学出版社，1988，195～202
[56] 黄道平，朱学峰，基于广义Hammerstain模型的多变量非线性预测控制，暨南大学学报(自然与医学版)，1998，19(1)：13～19
[57] 顾钟文，邹志云，罗文巍，一类非线性系统的预测控制算法，中国自动化学会首届过程控制科学报告会论文集，浙江大学出版社，1988，186～194
[58] K.Yeo. Yeong and C. Williams. Dennis, Bilinear model predictive control. Ind. Eng. Chem. Res.1987, Vol. 26: 2267～2274
[59] EN-BO Feng., et. al., New method for predictive controller design for bilinear systems. Int. J. Control, 1991, 53(1): 97～111
[60] 赵豫红等，双线性系统的一种预测控制算法，浙江大学学报(自然科学版)，1998，增刊：111～116
[61] 姚新元，钱积新，一种双线性系统的广义预测控制算法，浙江大学学报(自然科学版)1997，31(2)：231～236
[62] G.A. Dumont, et. al. Nonlinear adaptive generalized predictive control and applications, Edited by Clarke D., Oxford Science Publication,1994.
[63] 吴丽娟，张宁，基于Laguerre级数模型的自适应动态矩阵控制，控制与决策，1997，12(1)：37～42
[64] 席裕庚，王凡，非线性系统预测控制的多模型方法，自动化学报，1996，22(4)：456～460
[65] Yu-Geng Xi, et. al., Nonlinear Multi-Model predictive control. 13th Trennial World Congress of IFAC, San Fansico, 1996: 485～490
[66] A.Bjarne Foss.and Song-Bo Cong. Nonlinear MPC Based on Multi-Model for Distillation Columns, proc. of 14th IFAC World Congress, Beijing 1999,
Vol．N：337～342
[67] 王伟，李晓理，多模型自适应控制，科学出版社，2000
[68] 陈福祥，余晖，模糊预测控制机理研究，武汉工业大学学报，1997，Vol．19(1)：48～52．
[69] 谢克明，马小军，模糊预测控制的实现形式，太原理工大学学报，1999 Vol．30(6)：575～579
[70] 褚健，潘红华等，预测控制现状和展望，机电工程，1999，Vol．5：3～7
[71] 李少远，席裕庚等，智能控制的新进展，控制与决策，2000，Vol．15(1)：1～5
[72] 席裕庚，耿晓军等，预测控制性能的新进展，控制理路及其应用，2000，Vol．17(4)：469～475
[73] 陈虹，刘志远等，非线性模型预测控制的现状和问题，控制与决策，2000， Vol．16(4)：385～391
[74] 达鹏飞等，针对造纸过程大时延的模糊预测控制，第二届华人智能预测控制自动化大会论文集，1997：1794～1798
[75] Culal Batur. et al. Predictive fuzzy expert controllers. Computers in Eng. 1991,20(2): 199～209
[76] 庞富胜．模糊—线性复合控制，刘增良主编．模糊技术与应用选编(1)，北京航空航天大学出版社，1997：64～69
[77] 李少远，基于模糊推理和广义预测的组合控制，南开大学学报，1997，Vol．30(2)：20～24
[78] 徐立鸿，施建华，基于对象定量和定性信息的组合预测控制，自动化学报，1997，23(2)：257～260
[79] 雎刚，陈来九，模糊预测控制及其在过程汽温控制中的应用，中国电机工程学报，1996，Vol. 16(1)：17～21
[80] 乔俊飞，孙维明等，基于模糊预测的自适应控制应用，电工技术学报，2000，Vol．15(4)：67～70
[81] Oliver Hacker et. al. Nonlinear system identification and predictive control of a heat exchange based on local fuzzy models, proc. of ACC, 1997, pp: 3294～3298
[82] Martin Fisecher et.al. Nonlinear predictive control based on the extraction of step response models from Takagi-Sugeno fuzzy systems, proc. of ACC, 1997,
pp:2878～2882
[83] Jang-Hwan Kim, Generalized predictive control using fuzzy neural network model, proc. of 1994 IEEE conf. on Neural Network, 1994, pp(4): 2596～2598
[84] Igor Skrjanc and Drago Matko, Predictive functional control based on fuzzy model for heat-exchanger pilot plant, IFAC 14th Triennial World Congress, Beijing, P.R.China, 1999, pp: 341～345
[85] Katarina Kavsek-Biasizzo et.al. Fuzzy Predictive Control of Highly Nonlinear PH process. Computers Chem. Engng. Vol.21. 1997, pp:s613～s618
[86] ZhangHuaguang et. al. Mulitivariable FGPC controller and its performance analysis. IFAC 14th Triennial World Congress, Beijing, P.R.China. pp:223～228
[87] Doris Saez and Aldo Cipriano, Fuzzy models based economic predictive control for a combined cycle power plant boiler, proc. of the 1999 Int. sym. on Intelligent Control/Intelligent Systems and Semiotics, Cambridge, 1999, pp: 417～422
[88] Doris Saez and Aldo Cipriano, Design of fuzzy model based predictive controllers and its application to an inverted pendulum. 1997 IEEE Int. Conf. on Fuzzy Syst., pp: 915～918
[89] 邹健，诸静，模糊预测函数控制在水泥回转窑分解炉温控系统中的应用研究，硅酸盐学报，2001，Vol．29(4)：318～321
[90] Y.L.Huang et. al. Fuzzy model predictive control. 2000, Vol.8(6).pp:665～667
[91] U.Kaymak et. al. A comparative study of fuzzy and conventional criteria in model-based predictive control. Fuzzy-IEEE'97, Barcelona, Spain, July 1997, pp: 907～914
[92] J.M.Sousa., et. al. Adaptive decision alternatives in fuzzy predictive control, proc. of the 1998 IEEE on Fuzzy systems ,1998, pp. 698～918
[93] M.A.Goodrich, et.al. A theory of satisficing decisions and control, IEEE Trans. Syst., Man, Cybern. A, 1998, Vol.28,pp: 763～779
[94] M.A.Goodrich, et.al. Model predictive satisficing fuzzy logic control, IEEE Trans. Fuzzy syst. 1999, Vol. 7, pp:319～332
[95] Evelio Hemandez and Yaman Arkun, Neural Network Modeling and an Extended DMC Algorithm to Control Nonlinear Systems. ACC 1990, Vol.3, pp2454-2459
[96] 谢永斌，罗忠等，基于连续映射小脑模型的广义预测控制算法，控制理论及应用，1997．12，Vol．14(6)：842～846
[97] Jixian Zhan, Masaru Ishidu, The multi-step predictive control of nonlinear SISO process with a neural model predictive control(WMPC) method, Computers Chem. Engng. 1997, Vol.21(2): 201～210
[98] 徐湘元，毛宗源，非线性时延对象的神经网络控制，模式识别与人工智能，2000，Vol．13(2)：138～141
[99] Martin Pottmann and Dale. E. Seborg, A nonlinear predictive control strategy based on radial basis function models. Computers Chem. Engng. 1997 Vol. 21(9): 308～311
[100] Yu M. Zhang and Rodovan Kovacevic, Nerofuzzy Model-based predictive control of weld fusion zone ceomery. IEEE Trans. on Fuzzy Systems. 1998, Vol. 6(3): 389～401
[101] S. C. Shin and S. B. Park, GA-based predictive control for nonlinear processes, Electronics Letters, 1st Oct. 1998, Vol.34(20): 1980～1981
[102] D. R. Ramirez, et. al. Nonlinear MBPC for Mobile Robot Navigation Using Genetic Algorithms. proc. of IEEE int. conf. on Robotcs&Automation. Detroit, Michigan, May 1999, pp: 2452～2457
[103] I.Woolley, et. al. Intelligent control tookit for the CONNOISSEUR~(TM) advanced control package. IEE 5tH Int. Conf. on FACTORY 2000, Apr. 1997 pp288～295
[104] J.M.Sousa and U.Kaymak, Model predictive control Using Fuzzy Decision Functions. IEEE Trans. On SMC, Feb 2001, Part B:Vol.31(1): 54～65
[105] 王万良，动态矩阵控制的改进及其神经网络实现，浙江大学学报(自然科学版)增刊，1995,Nov：105～109
[106] Mohamed Laid Hadjili and Vincent Wertz, Generalized predictive control using Takagi-Sugeno fuzzy models, Proc. Of 1999 IEEE Int. Sym. On Intelligent Control/Intelligent Systems and Semiotics, Sup 15-17, 1999: 406～410
[107] E. F. Camacho, etc., Adaptive Generalized Predictive Control of a Distributed Collector Field. IEEE Trans. on Control Systems Technology,1994, 2: 462-468;
[108] E. F. Camacho and Carlos Bordons, Model Predictive Control in Process Industry, Springer-Verlag, 1994
[109] R. M.Miller, etc., Predictive PID, ISA Transactions, 1999(38): 11～23
[110] 杨马英，王树青，王骥程，大时滞过程的预测控制—催化裂化反应再生系统原料预热回路控制，控制理论及应用，2000，Vol．17(1)：143～146
[111] 李为民，黄田，数控铣削过程的广义预测控制，中国机械工程，1998，9(5)：48～51
[112] 肖明波，邵惠鹤，多模型DMC及其在水力测功器系统中的应用，自动化学报，1997，23(1)：103～106
[113] P. A. Wisnewski and F. J. Doyle, Model-based predictive control studies for a continuous pulp digester, IEEE trans. On control systems technology, 2001 Vol.9(3):435～444
[114] R. Hoper and M. Otter, A versatile c++ toolbox for model based, real time control systems of robotics manipulators, of IEEE proc. In Intelligent Robots and Systems, 2001(4): 2208～2214,
[115] Y.B. Veryha and L. Kourtch, Neural network based manipulator control with time-optimal robot path tracking, proc. of the IEEE Int. Conf. On Control Applications, 2000, pp: 748～753.
[116] Negm and M.M.M, Torque optimized speed control of a 3-phase induction motor, Proc. of International Conference on Power System Technology, 2000, Vo1.(1): 67～72,
[117] B.Bulut, etc., Industrial application of model based predictive control as a supervisory system, proc. of the ACC, Chicago, 2000, pp: 3763～3767
[118] 窦汝振 许镇琳等，电气自动化，预测控制在异步电机直接转矩控制系统中的应用研究，2001，(4)：7～9
[119] 刘妹琴、邓燕妮，基于时延RBFNN模型预测控制的交流伺服系统，中国电机工程学报，2000，20(1)：7～10