Statistical learning control of uncertain systems: theory and algorithms

被引:9
|
作者
Koltchinskii, V [1 ]
Abdallah, CT
Ariola, M
Dorato, P
机构
[1] Univ New Mexico, Dept Math & Stat, Albuquerque, NM 87131 USA
[2] Univ New Mexico, Dept EECE, Albuquerque, NM 87131 USA
[3] Univ Naples Federico II, Dipartimento Informat & Sistemist, Naples, Italy
基金
美国国家科学基金会;
关键词
empirical processes; statistical learning; robust control; optimization;
D O I
10.1016/S0096-3003(99)00283-0
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
It has recently become clear that many control problems are too difficult to admit analytic solutions. New results have also emerged to show that the computational complexity of some "solved" control problems is prohibitive, Many of these control problems can be reduced to decidability problems or to optimization questions. Even though such questions may be too difficult to answer analytically, or may not be answered exactly given a reasonable amount of computational resources, researchers have shown that we can "approximately'' answer these questions "most of the time", and have "high confidence" in the correctness of the answers. (C) 2001 Elsevier Science Inc. All rights reserved.
引用
收藏
页码:31 / 43
页数:13
相关论文
共 50 条
  • [41] Game theory, learning, and control systems
    Jeff S.Shamma
    [J]. National Science Review, 2020, 7 (07) : 1118 - 1119
  • [42] Game theory, learning, and control systems
    Shamma J.S.
    [J]. National Science Review, 2020, 7 (07): : 1118 - 1119
  • [43] One method for robust control of uncertain systems: Theory and practice
    Leitmann, G
    [J]. KYBERNETIKA, 1996, 32 (01) : 43 - 62
  • [44] Lyapunov stability theory based control of uncertain dynamical systems
    Leitmann, G.
    [J]. Mathematical Modelling and Scientific Computing, 1993, 2 (sectiob):
  • [45] Statistical machine-learning-based predictive control of uncertain nonlinear processes
    Wu, Zhe
    Alnajdi, Aisha
    Gu, Quanquan
    Christofides, Panagiotis D.
    [J]. AICHE JOURNAL, 2022, 68 (05)
  • [46] Adaptive robust iterative learning control for uncertain robotic systems
    Yang, SY
    Fan, XP
    Luo, A
    [J]. PROCEEDINGS OF THE 4TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-4, 2002, : 964 - 968
  • [47] Spatial iterative learning control for a class of uncertain motion systems
    Liu J.-L.
    Dong X.-M.
    Xue J.-P.
    Wang H.-T.
    [J]. Dong, Xin-Min (dongxinmin@139.com), 2017, South China University of Technology (34): : 197 - 204
  • [48] Repetitive Learning Control for a Class of Nonlinearly Parameterized Uncertain Systems
    Sun, Yunping
    Li, Junmin
    Wang, Yuanliang
    Zhao, Weiping
    [J]. 2008 7TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-23, 2008, : 8116 - 8121
  • [49] A neural network learning algorithm for uncertain nonlinear control systems
    Wang, Bo
    Wen, Guangjun
    [J]. DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS, 2007, 14 : 528 - 531
  • [50] Asymptotic learning control for a class of cascaded nonlinear uncertain systems
    Qu, ZH
    Xu, JX
    [J]. IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2002, 47 (08) : 1369 - 1376