2.793

2018影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

智能优化控制:概述与展望

辛斌 陈杰 彭志红

辛斌, 陈杰, 彭志红. 智能优化控制:概述与展望. 自动化学报, 2013, 39(11): 1831-1848. doi: 10.3724/SP.J.1004.2013.01831
引用本文: 辛斌, 陈杰, 彭志红. 智能优化控制:概述与展望. 自动化学报, 2013, 39(11): 1831-1848. doi: 10.3724/SP.J.1004.2013.01831
XIN Bin, CHEN Jie, PENG Zhi-Hong. Intelligent Optimized Control: Overview and Prospect. ACTA AUTOMATICA SINICA, 2013, 39(11): 1831-1848. doi: 10.3724/SP.J.1004.2013.01831
Citation: XIN Bin, CHEN Jie, PENG Zhi-Hong. Intelligent Optimized Control: Overview and Prospect. ACTA AUTOMATICA SINICA, 2013, 39(11): 1831-1848. doi: 10.3724/SP.J.1004.2013.01831

智能优化控制:概述与展望


DOI: 10.3724/SP.J.1004.2013.01831
详细信息
    作者简介:

    陈杰 北京理工大学自动化学院教授.1986 年, 1996 年和2000 年分别获得北京理工大学控制科学与工程专业学士学位、硕士学位和博士学位. 主要研究方向为复杂系统智能控制与优化. E-mail: chenjie@bit.edu.cn

  • 基金项目:

    国家杰出青年科学基金(60925011),国家自然科学基金重大国际合作项目(61120106010),国家自然科学基金(61203078),北京市教育委员会共建项目专项资助

Intelligent Optimized Control: Overview and Prospect

More Information
  • Fund Project:

    Supported by National Science Fund for Distinguished Young Scholars (60925011), the Major International (Regional) Joint Research Program of China (61120106010), National Natural Science Foundation of China (61203078), and Beijing Education Committee Cooperation Building Foundation Project

  • 摘要: 从模糊优化控制、神经网络优化控制、模糊神经网络优化控制、基于智能优化方法的优化控制等角度, 对国内外与智能优化控制(Intelligent optimized control, IOC)密切相关的研究进行了综述, 在此基础上对智能优化控制的相关概念进行了深入分析, 并对智能优化控制方法进行了分类, 最后, 对与智能优化控制有关的一些重要问题进行了讨论, 并展望了智能优化控制研究未来的发展.
  • [1] Saridis G N. Analytic formulation of the principle of increasing precision with decreasing intelligence for intelligent machines. Automatica, 1989, 25(3): 461-467
    [2] Cai Zi-Xing. Intelligent Control Principles and Applications. Beijing: Tsinghua University Press, 2007(蔡自兴. 智能控制原理与应用. 北京: 清华大学出版社, 2007)
    [3] Antsaklis P J. Intelligent control. Encyclopedia of Electrical and Electronics Engineering. New York: John Wiley & Sons, 1997
    [4] Yi Ji-Kai, Hou Yuan-Bin. Intelligent Control. Beijing: Beijing University of Technology Press, 1999(易继锴, 侯媛彬. 智能控制技术. 北京: 北京工业大学出版社, 1999)
    [5] Wang Yao-Nan. Intelligent Control System. Changsha: Hunan University Press, 2006(王耀南. 智能控制系统. 长沙: 湖南大学出版社, 2006)
    [6] Sun Zeng-Qi, Deng Zhi-Dong, Zhang Zai-Xing. Intelligent Control Theory and Technology. Beijing: Tsinghua University Press, 2011(孙增圻, 邓志东, 张再兴. 智能控制理论与技术. 北京: 清华大学出版社, 2011)
    [7] Linkens D A, Nyongesa H O. Learning systems in intelligent control: an appraisal of fuzzy, neural and genetic algorithm control applications. IEE Proceedings—— Control Theory and Applications, 1996, 143(4): 367-386
    [8] Leondes C T, Mendel J M. Artificial intelligent control. Technical Report 4336, McDonnell-Douglas Astronautics Corporation, USA, 1967
    [9] Fu K S. Learning control systems and intelligent control systems: an intersection of artificial intelligence and automatic control. IEEE Transactions on Automatic Control, 1971, 16(1): 70-72
    [10] Wang L X, Mendel J M. Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Transactions on Neural Networks, 1992, 3(5): 807-814
    [11] Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Networks, 1989, 2(5): 359-366
    [12] Zheng Nan-Ning, Jia Xin-Chun, Yuan Ze-Jian. A survey of control science and technology. Acta Automatica Sinica, 2002, 28(S1): 7-17(郑南宁, 贾新春, 袁泽剑. 控制科学与技术的发展及其思考. 自动化学报, 2002, 28(S1): 7-17)
    [13] Huang Lin. Future development in control science: why, what and strategy. Acta Automatica Sinica, 2013, 39(2): 97-100(黄琳. 为什么做, 做什么和发展战略——控制科学学科发展战略研讨会约稿前言. 自动化学报, 2013, 39(2): 97-100)
    [14] Zheng Da-Zhong. Development of control science and its revelation. Studies in Dialectics of Nature, 1986, 2(6): 57-62(郑大钟. 控制科学的发展及其启示. 自然辩证法研究, 1986, 2(6): 57-62)
    [15] Huang Lin, Peng Zhong-Xing, Wang Jin-Zhi. Control science: inspired by applications. Science & Technology Review, 2011, 29(17): 72-79(黄琳, 彭中兴, 王金枝. 控制科学——与需俱进的科学. 科技导报, 2011, 29(17): 72-79)
    [16] Coello C A C. Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 2002, 191(11-12): 1245-1287
    [17] Wang Ling, Liu Bo. Particle Swarm Optimization and Scheduling Algorithms. Beijing: Tsinghua University Press, 2008(王凌, 刘波. 微粒群优化与调度算法. 北京: 清华大学出版社, 2008)
    [18] Yan Ai-Jun, Chai Tian-You, Yue Heng. Multivariable intelligent optimizing control approach for shaft furnace roasting process. Acta Automatica Sinica, 2006, 32(4): 636-640(严爱军, 柴天佑, 岳恒. 竖炉焙烧过程的多变量智能优化控制. 自动化学报, 2006, 32(4): 636-640)
    [19] Huang Yin-Rong, Zhang Shao-De. Dissolved oxygen intelligent optimization control system in the aeration tank of wastewater treatment. Information and Control, 2011, 40(3): 393-400(黄银蓉, 张绍德. 污水处理曝气池溶解氧智能优化控制系统. 信息与控制, 2011, 40(3): 393-400)
    [20] Bai Rui, Tong Shao-Cheng, Chai Tian-You. Intelligent optimal control of the raw slurry producing process in the alumina production. Control and Decision, 2013, 28(4): 525-530(白锐, 佟绍成, 柴天佑. 氧化铝生料浆制备过程的智能优化控制方法. 控制与决策, 2013, 28(4): 525-530)
    [21] Xu Chen-Hua, Wu Min. Intelligent integrated optimization control of quality and quantity for lead-zinc sintering process. Control Theory & Applications, 2008, 25(4): 688-692(徐辰华, 吴敏. 铅锌烧结过程质量产量的智能集成优化控制. 控制理论与应用, 2008, 25(4): 688-692)
    [22] Mo Ju-Hua, Huang Min, Wang Xing-Wei. Application of a pull strategy based on fuzzy control for production control of assembly line. Acta Automatica Sinica, 2011, 37(1): 118-123(莫巨华, 黄敏, 王兴伟. 基于模糊控制的拉式策略在装配生产控制中的应用. 自动化学报, 2011, 37(1): 118-123)
    [23] Tian Yi, Zhang Xin, Zhang Liang, Zhang Xin. Fuzzy control strategy for hybrid electric vehicle based on neural network identification of driving conditions. Control Theory & Applications, 2011, 28(3): 363-369(田毅, 张欣, 张良, 张昕. 神经网络工况识别的混合动力电动汽车模糊控制策略. 控制理论与应用, 2011, 28(3): 363-369)
    [24] He Jin-Bao, Guo Shuai, He Yong-Yi, Fang Ming-Lun. A fuzzy tension-controller based on genetic algorithm. Control Theory & Applications, 2009, 26(3): 243-248(何金保, 郭帅, 何永义, 方明伦. 基于遗传优化的张力模糊控制. 控制理论与应用, 2009, 26(3): 243-248)
    [25] Duan Ping, Zhang Jian-Chang, Ding Cheng-Jun, Zhang Ming-Lu. The fuzzy genetic algorithm for the mobile robot's wall tracking control. Control Theory & Applications, 2006, 23(3): 416-420(段萍, 张建畅, 丁承君, 张明路. 基于模糊遗传算法的移动机器人墙跟踪控制策略. 控制理论与应用, 2006, 23(3): 416-420)
    [26] Hu Yue-Ming, Qi Hao-Feng, Wang Jian. The application of genetic algorithm based P-F-PI controller in position control of robotic manipulator. Control Theory & Applications, 2000, 17(5): 716-720(胡跃明, 戚浩峰, 王建. 基于遗传算法的P-F-PI控制器在机器人手臂定位控制中的应用. 控制理论与应用, 2000, 17(5): 716-720)
    [27] Pal T, Pal N R. SOGARG: a self-organized genetic algorithm-based rule generation scheme for fuzzy controllers. IEEE Transactions on Evolutionary Computation, 2003, 7(4): 397-415
    [28] Martínez R, Castillo O, Aguilar L T. Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms. Information Sciences, 2009, 179(13): 2158-2174
    [29] Cheng R G, Chang C J. Design of a fuzzy traffic controller for ATM networks. IEEE-ACM Transactions on Networking, 1996, 4(3): 460-469
    [30] Alcalá R, Casillas J, Cordón O, González A, Herrera F. A genetic rule weighting and selection process for fuzzy control of heating, ventilating and air conditioning systems. Engineering Applications of Artificial Intelligence, 2005, 18(3): 279-296
    [31] Chang W, Park J B, Joo Y H. GA-based intelligent digital redesign of fuzzy-model-based controllers. IEEE Transactions on Fuzzy Systems, 2003, 11(1): 35-44
    [32] Castillo O, Valdez F, Melin P. Hierarchical genetic algorithms for topology optimization in fuzzy control systems. International Journal of General Systems, 2007, 36(5): 575-591
    [33] Montazeri-Gh M, Safari A. Tuning of fuzzy fuel controller for aero-engine thrust regulation and safety considerations using genetic algorithm. Aerospace Science and Technology, 2011, 15(3): 183-192
    [34] Kharrati H, Khanmohammadi S, Zeiaee A, Navarbaf A, Alizadeh G. Design of optimized fuzzy model-based controller for nonlinear systems using hybrid intelligent strategies. Proceedings of the Institution of Mechanical Engineers, Part I—— Journal of Systems and Control Engineering, 2012, 226(19): 1152-1165
    [35] Bezine H, Derbel N, Alimi A M. Fuzzy control of robot manipulators: some issues on design and rule base size reduction. Engineering Applications of Artificial Intelligence, 2002, 15(5): 401-416
    [36] Jain R, Sivakumaran N, Radhakrishnan T K. Design of self tuning fuzzy controllers for nonlinear systems. Expert Systems with Applications, 2011, 38(4): 4466-4476
    [37] Ding Yong-Sheng, Ren Li-Hong, Shao Shi-Huang. Automatic design of Takagi-Sugeno fuzzy controllers by a new DNA-based evolutionary algorithm. Acta Automatica Sinica, 2001, 27(4): 510-520 (丁永生, 任立红, 邵世煌. 采用新的DNA进化算法自动设计Takagi-Sugeno模糊控制器.自动化学报, 2001, 27(4): 510-520)
    [38] Tsakonas A. Local and global optimization for Takagi-Sugeno fuzzy system by memetic genetic programming. Expert Systems with Applications, 2013, 40(8): 3282-3298
    [39] Chen Jie, Pan Feng, Cai Tao. Acceleration factor harmonious particle swarm optimizer. International Journal of Automation and Computing, 2006, 3(1): 41-46
    [40] Hao Wan-Jun, Qiang Wen-Yi, Chai Qing-Xuan, Hu Lin-Xian. Design of fuzzy controller based on particle swarm optimization. Control and Decision, 2007, 22(5): 585-588(郝万君, 强文义, 柴庆宣, 胡林献. 基于粒子群优化的一类模糊控制器设计. 控制与决策, 2007, 22(5): 585-588)
    [41] Castillo O, Martínez-Marroquín R, Melin P, Valdez F, Soria J. Comparative study of bio-inspired algorithms applied to the optimization of type-1 and type-2 fuzzy controllers for an autonomous mobile robot. Information Sciences, 2012, 192: 19-38
    [42] Khooban M H, Soltanpour M R. Swarm optimization tuned fuzzy sliding mode control design for a class of nonlinear systems in presence of uncertainties. Journal of Intelligent & Fuzzy Systems, 2013, 24(2): 383-394
    [43] Pan I, Korre A, Das S, Durucan S. Chaos suppression in a fractional order financial system using intelligent regrouping PSO based fractional fuzzy control policy in the presence of fractional Gaussian noise. Nonlinear Dynamics, 2012, 70(4): 2445-2461
    [44] Jiang H M, Kwong C K, Chen Z Q, Ysim Y C. Chaos particle swarm optimization and T-S fuzzy modeling approaches to constrained predictive control. Expert Systems with Applications, 2012, 39(1): 194-201
    [45] Lu X J, Li H X, Yuan X. PSO-based intelligent integration of design and control for one kind of curing process. Journal of Process Control, 2010, 20(10): 1116-1125
    [46] Feng H M, Chen C Y, Horng J H. Intelligent omni-directional vision-based mobile robot fuzzy systems design and implementation. Expert Systems with Applications, 2010, 37(5): 4009-4019
    [47] Yao X. Evolving artificial neural networks. Proceedings of the IEEE, 1999, 87(9): 1423-1447
    [48] Li Min-Yuan, Du Yan-Li. Composite neural networks adaptive control system of temperature based on GA learning. Control Theory & Applications, 2004, 21(2): 242-246(李敏远, 都延丽. 基于遗传算法学习的复合神经网络自适应温度控制系统. 控制理论与应用, 2004, 21(2): 242-246)
    [49] Reil T, Husbands P. Evolution of central pattern generators for bipedal walking in a real-time physics environment. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 159-168
    [50] Hung S L, Adeli H. A parallel genetic neural network learning algorithm for MIMD shared-memory machines. IEEE Transactions on Neural Networks, 1994, 5(6): 900-909
    [51] Song Ying, Chen Zeng-Qiang, Yuan Zhu-Zhi. A nonlinear predictive controller based on chaos optimization. Control Theory & Applications, 2007, 24(4): 561-564(宋莹, 陈增强, 袁著祉. 基于混沌优化的非线性预测控制器. 控制理论与应用, 2007, 24(4): 561-564)
    [52] Becerikli Y, Konar A F, Samad T. Intelligent optimal control with dynamic neural networks. Neural Networks, 2003, 16(2): 251-259
    [53] Kosmatopoulos E B, Kouvelas A. Large scale nonlinear control system fine-tuning through learning. IEEE Transactions on Neural Networks, 2009, 20(6): 1009-1023
    [54] Liu Xiang-Jie, Zhou Xiao-Xin, Chai Tian-You. Status and development of fuzzy control. Information and Control, 1999, 28(4): 283-292(刘向杰, 周孝信, 柴天佑. 模糊控制研究的现状与新发展. 信息与控制, 1999, 28(4): 283-292)
    [55] Li Xiang-Fei, Zou En, Zhang Tai-Shan. Optimization design of fuzzy neural networks controller parameter based on chaos. Control and Decision, 2002, 17(3): 320-323(李祥飞, 邹恩, 张泰山. 一种模糊神经网络控制器参数的混沌优化设计. 控制与决策, 2002, 17(3): 320-323)
    [56] Du Yan-Li, Wu Qing-Xian, Jiang Chang-Sheng, Zhou Li. Improved cooperative particle swarm optimizer for design of fuzzy neural network control system. Control and Decision, 2008, 23(12): 1327-1337(都延丽, 吴庆宪, 姜长生, 周丽. 改进协同微粒群优化的模糊神经网络控制系统设计. 控制与决策, 2008, 23(12): 1327-1337)
    [57] Becerikli Y, Oysal Y, Konar A F. Trajectory priming with dynamic fuzzy networks in nonlinear optimal control. IEEE Transactions on Neural Networks, 2004, 15(2): 383-394
    [58] Rajapakse A, Furuta K, Kondo S. Evolutionary learning of fuzzy logic controllers and their adaptation through perpetual evolution. IEEE Transactions on Fuzzy Systems, 2002, 10(3): 309-321
    [59] Liao Y X, She J H, Wu M. Integrated hybrid-PSO and fuzzy-NN decoupling control for temperature of reheating furnace. IEEE Transactions on Industrial Electronics, 2009, 56(7): 2704-2714
    [60] Sun Qiang, Cheng Ming. Nonlinear modeling for doubly salient permanent magnetic motor based on fuzzy neural network. Control Theory & Applications, 2007, 24(4): 601-606(孙强, 程明. 基于模糊神经网络的双凸极永磁电机非线性建模. 控制理论与应用, 2007, 24(4): 601-606)
    [61] Uddin M N, Abido M A, Rahman M A. Development and implementation of a hybrid intelligent controller for interior permanent-magnet synchronous motor drives. IEEE Transactions on Industry Applications, 2004, 40(1): 68-76
    [62] Lin W M, Hong C M, Cheng F S. Design of intelligent controllers for wind generation system with sensorless maximum wind energy control. Energy Conversion and Management, 2011, 52(2): 1086-1096
    [63] Ayoubi M A, Tai L C. Intelligent control of a large variable speed wind turbine. Journal of Solar Energy Engineering-Transactions of the ASME, 2012, 134(1): 011001, doi:  10.1115/1.4004979
    [64] Lee Y, Zak S H. Designing a genetic neural fuzzy antilock-brake-system controller. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 198-211
    [65] Chen Z M, Meng W J, Zhang J G. Intelligent anti-swing control for bridge crane. Journal of Central South University, 2012, 19(10): 2774-2781
    [66] Lin Ye-Jin, Ren Guang. New radial basis function fuzzy network controller based on genetic algorithms for ship control. Control Theory & Applications, 2004, 21(6): 1036-1040(林叶锦, 任光. 遗传优化的径向基函数船舶模糊控制器. 控制理论与应用, 2004, 21(6): 1036-1040)
    [67] Chen L H, Chiang C H. New approach to intelligent control systems with self-exploring process. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2003, 33(1): 56-66
    [68] Singh N A, Muraleedharan K A, Gomathy K. Damping of low frequency oscillations in power system network using swarm intelligence tuned fuzzy controller. International Journal of Bio-inspired Computation, 2010, 2(1): 1-8
    [69] Mishra S, Dash P K, Hota P K, Tripathy M. Genetically optimized neuro-fuzzy IPFC for damping modal oscillations of power system. IEEE Transactions on Power Systems, 2002, 17(4): 1140-1147
    [70] Kuang Xian-Yan, Xu Lun-Hui, Huang Yan-Guo. Traffic signal bus-priority control strategy and intelligent control method. Control Theory & Applications, 2012, 29(10): 1284-1290(邝先验, 许伦辉, 黄艳国. 交通信号公交优先控制策略及智能控制方法. 控制理论与应用, 2012, 29(10): 1284-1290)
    [71] Wu Xing, Lou Pei-Huang, Tang Dun-Bing. Multi-objective optimization for PID parameter based on elitist-evolution guidance. Control Theory & Applications, 2010, 27(9): 1235-1239(武星, 楼佩煌, 唐敦兵. 基于精英进化导向的多目标PID参数优化. 控制理论与应用, 2010, 27(9): 1235-1239)
    [72] Yang Zhi, Chen Zhi-Tang, Fan Zheng-Ping, Li Xiao-Dong. Tuning of PID controller based on improved particle-swarm-optimization. Control Theory & Applications, 2010, 27(10): 1345-1352(杨智, 陈志堂, 范正平, 李晓东. 基于改进粒子群优化算法的PID控制器整定. 控制理论与应用, 2010, 27(10): 1345-1352)
    [73] Li Zhong-Hua, Zhang Yu-Nong, Tan Hong-Zhou, Chen Zhuo-Yi. An enhanced artificial immune network with elitist-learning capability for optimization problems. Control Theory & Applications, 2009, 26(3): 283-290(李中华, 张雨浓, 谭洪周, 陈卓怡. 一类具有精英学习能力的增强型人工免疫网络优化算法. 控制理论与应用, 2009, 26(3): 283-290)
    [74] Mukherjee V, Ghoshal S P. Intelligent particle swarm optimized fuzzy PID controller for AVR system. Electric Power Systems Research, 2007, 77(12): 1689-1698
    [75] Meng An-Bo, Ye Lu-Qing, Yin Hao, Liang Hong-Zhu, Fu Chuang, Cheng Yuan-Chu. Application of genetic algorithm in adaptive governor with variable PID parameters. Control Theory & Applications, 2004, 21(3): 398-404(孟安波, 叶鲁卿, 殷豪, 梁宏柱, 傅闯, 程远楚. 遗传算法在水电机组调速器PID参数优化中的应用. 控制理论与应用, 2004, 21(3): 398-404)
    [76] Lacca G, Caraffini F, Neri F. Memory-saving memetic computing for path-following mobile robots. Applied Soft Computing, 2013, 13(4): 2003-2016
    [77] Das S, Pan I, Das S, Gupta A. Master-slave chaos synchronization via optimal fractional order PIγDμ controller with bacterial foraging algorithm. Nonlinear Dynamics, 2012, 69(4): 2193-2206
  • [1] 李海波, 柴天佑, 赵大勇. 混合选别浓密机底流矿浆浓度和流量区间智能切换mm控制方法[J]. 自动化学报, 2014, 40(9): 1967-1975. doi: 10.3724/SP.J.1004.2014.01967
    [2] 刘德荣, 李宏亮, 王鼎. 基于数据的自学习优化控制:研究进展与展望[J]. 自动化学报, 2013, 39(11): 1858-1870. doi: 10.3724/SP.J.1004.2013.01858
    [3] 柴天佑, 丁进良, 王宏, 苏春翌. 复杂工业过程运行的混合智能优化控制方法[J]. 自动化学报, 2008, 34(5): 505-515. doi: 10.3724/SP.J.1004.2008.00505
    [4] 严爱军, 柴天佑, 岳恒. 竖炉焙烧过程的多变量智能优化控制[J]. 自动化学报, 2006, 32(4): 636-640.
    [5] 袁著祉, 陈增强, 李翔. 联接主义智能控制综述[J]. 自动化学报, 2002, 28(增刊): 38-59.
    [6] 李智斌, 吴宏鑫, 解永春, 王晓磊, 于志杰, 王颖. 航天器智能控制实验平台[J]. 自动化学报, 2001, 27(5): 695-699.
    [7] 吴宏鑫, 解永春, 李智斌, 何英姿. 基于对象特征模型描述的智能控制[J]. 自动化学报, 1999, 25(1): 9-17.
    [8] 王亦兵, 韩曾晋, 贺国光. 城市高速公路交通控制综述[J]. 自动化学报, 1998, 24(4): 484-496.
    [9] 黄苏南, 邵惠鹤, 钱积新. 一种智能控制器[J]. 自动化学报, 1997, 23(1): 116-120.
    [10] 牛培峰. 自整定智能控制器及其应用[J]. 自动化学报, 1996, 22(2): 214-218.
    [11] 王桂珠, 贺国光, 马寿峰. 一种新型的自学习智能式城市交通实时控制系统[J]. 自动化学报, 1995, 21(4): 424-430.
    [12] 涂亚庆, 李祖枢. 一种新型的仿人智能控制器的设计方法[J]. 自动化学报, 1994, 20(5): 616-621.
    [13] 罗公亮, 卢强. 智能控制与常规控制[J]. 自动化学报, 1994, 20(3): 324-332.
    [14] 王顺晃, 邵启伟, 张广厚. 带自学习的非晶制带钢水液位智能控制[J]. 自动化学报, 1993, 19(1): 96-100.
    [15] 姜玉宪, 姜秀杰, 张建洲. 变指令智能控制模式及其在预测拦截中的应用[J]. 自动化学报, 1993, 19(6): 711-714.
    [16] 俞忠原, 施小成. 潜艇自动变深的智能控制及其实现[J]. 自动化学报, 1992, 18(3): 379-382.
    [17] 周德泽, 袁南儿, 李敏. 工业智能控制器及在同步剪切中的应用[J]. 自动化学报, 1991, 17(1): 115-117.
    [18] 朱淼良, 陈纯, 傅永建. CREGS--用于智能控制的多专家系统[J]. 自动化学报, 1991, 17(5): 597-600.
    [19] 舒迪前, 刘宏才, 吴保亮, 王京, 郑福建, 黄程阳. 智能自适应控制及其在轧辊退火炉上的应用[J]. 自动化学报, 1991, 17(2): 207-214.
    [20] 李祖枢, 徐鸣, 周其鉴. 一种新型的仿人智能控制器(SHIC)[J]. 自动化学报, 1990, 16(6): 503-509.
  • 加载中
计量
  • 文章访问数:  2228
  • HTML全文浏览量:  114
  • PDF下载量:  3300
  • 被引次数: 0
出版历程
  • 收稿日期:  2013-07-04
  • 修回日期:  2013-08-28
  • 刊出日期:  2013-11-20

智能优化控制:概述与展望

doi: 10.3724/SP.J.1004.2013.01831
    基金项目:

    国家杰出青年科学基金(60925011),国家自然科学基金重大国际合作项目(61120106010),国家自然科学基金(61203078),北京市教育委员会共建项目专项资助

    作者简介:

    陈杰 北京理工大学自动化学院教授.1986 年, 1996 年和2000 年分别获得北京理工大学控制科学与工程专业学士学位、硕士学位和博士学位. 主要研究方向为复杂系统智能控制与优化. E-mail: chenjie@bit.edu.cn

摘要: 从模糊优化控制、神经网络优化控制、模糊神经网络优化控制、基于智能优化方法的优化控制等角度, 对国内外与智能优化控制(Intelligent optimized control, IOC)密切相关的研究进行了综述, 在此基础上对智能优化控制的相关概念进行了深入分析, 并对智能优化控制方法进行了分类, 最后, 对与智能优化控制有关的一些重要问题进行了讨论, 并展望了智能优化控制研究未来的发展.

English Abstract

辛斌, 陈杰, 彭志红. 智能优化控制:概述与展望. 自动化学报, 2013, 39(11): 1831-1848. doi: 10.3724/SP.J.1004.2013.01831
引用本文: 辛斌, 陈杰, 彭志红. 智能优化控制:概述与展望. 自动化学报, 2013, 39(11): 1831-1848. doi: 10.3724/SP.J.1004.2013.01831
XIN Bin, CHEN Jie, PENG Zhi-Hong. Intelligent Optimized Control: Overview and Prospect. ACTA AUTOMATICA SINICA, 2013, 39(11): 1831-1848. doi: 10.3724/SP.J.1004.2013.01831
Citation: XIN Bin, CHEN Jie, PENG Zhi-Hong. Intelligent Optimized Control: Overview and Prospect. ACTA AUTOMATICA SINICA, 2013, 39(11): 1831-1848. doi: 10.3724/SP.J.1004.2013.01831
参考文献 (77)

目录

    /

    返回文章
    返回