• Electronics Optics & Control
  • Vol. 26, Issue 7, 40 (2019)
PU Jun, MA Qingliang, LI Yuandong, and GU Fan
Author Affiliations
  • [in Chinese]
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    DOI: 10.3969/j.issn.1671-637x.2019.07.008 Cite this Article
    PU Jun, MA Qingliang, LI Yuandong, GU Fan. H∞ Control of Nonlinear Systems with Input Constraints Based on Data-Driven Adaptive Dynamic Programming[J]. Electronics Optics & Control, 2019, 26(7): 40 Copy Citation Text show less
    References

    [1] BERTSEKAS D P. Dynamic programming and optimal control[M]. Belmont:Athena Scientific, 1995.

    [3] ZHANG H, WEI Q, LIU D.An iterative adaptive dynamic programming method for solving a class of nonlinear zero-sum differential game[J]. Automatica, 2011, 47(1): 207-214.

    [4] LIU D R, YANG X, WANG D, et al. Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints[J]. IEEE Transactions on Cybernetics, 2015, 45(7):1372-1385.

    [5] LUO B, WU H N.Computationally efficient simultaneous policy update algorithm for nonlinear H∞ state feedback control with Galerkin's method[J]. International Journal of Robust and Nonlinear Control, 2013, 23(7):991-1012.

    [6] YASINI S, KARIMPOUR A, SISTANI M B, et al. Online concurrent reinforcement learning algorithm to solve two-player zero-sum games for partially unknown nonlinear continuous-time systems[J]. International Journal of Adaptive Control and Signal Processing, 2015, 29(4):473-493.

    [7] LUO B, HUANG T, WU H N, et al. Data-driven H∞ control for nonlinear distributed parameter systems[J]. IEEE Transactions on Neural Networks & Learning Systems, 2015, 26(11): 2949-2961.

    [8] YANG X, LIU D, WEI Q, et al.Adaptive dynamic programming for H∞ control of constrained-input nonlinear systems[C]//Proceedings of the 34th Chinese Control Confe-rence, IEEE, 2015: 3027-3032.

    [9] ZHU Y H, ZHAO D B, LI X J, et al. Iterative adaptive dynamic programming for solving unknown nonlinear zero-sum game based on online data[J]. IEEE Transactions on Neural Networks & Learning Systems, 2017, 28(3): 714-725.

    [10] ZHANG Q C, ZHAO D B, ZHU Y H. Data-driven adaptive dynamic programming for continuous-time fully cooperative games with partially constrained inputs[J]. Neurocomputing, 2017, 238: 377-386.

    [11] TAPIA R A. The Kantorovich theorem for Newton's method[J]. The American Mathematical Monthly, 1971, 78(4): 389-392.

    [12] FINLAYSON B A. The method of weighted residuals and variational principles[M]. New York:Academic Press, Inc. 2014.

    PU Jun, MA Qingliang, LI Yuandong, GU Fan. H∞ Control of Nonlinear Systems with Input Constraints Based on Data-Driven Adaptive Dynamic Programming[J]. Electronics Optics & Control, 2019, 26(7): 40
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