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Volume 8 Issue 3
Mar.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2021  >  8(3): 617-627
Wei Zhao, Yanjun Liu and Lei Liu, "Observer-Based Adaptive Fuzzy Tracking Control Using Integral Barrier Lyapunov Functionals for A Nonlinear System With Full State Constraints," IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 617-627, Mar. 2021. doi: 10.1109/JAS.2021.1003877
Citation: Wei Zhao, Yanjun Liu and Lei Liu, "Observer-Based Adaptive Fuzzy Tracking Control Using Integral Barrier Lyapunov Functionals for A Nonlinear System With Full State Constraints," IEEE/CAA J. Autom. Sinica, vol. 8, no. 3, pp. 617-627, Mar. 2021. doi: 10.1109/JAS.2021.1003877
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Observer-Based Adaptive Fuzzy Tracking Control Using Integral Barrier Lyapunov Functionals for A Nonlinear System With Full State Constraints

doi: 10.1109/JAS.2021.1003877
Funds:  This work was supported in part by the National Natural Science Foundation of China (62025303, 61973147), and in part by the LiaoNing Revitalization Talents Program (XLYC1907050)
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    Abstract
  • A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints. The fuzzy logic system is used to design the approximator, which deals with uncertain and continuous functions in the process of backstepping design. The use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint, but also mixes the states and errors to directly constrain the state, reducing the conservativeness of the constraint satisfaction condition. Considering that the states in most nonlinear systems are immeasurable, a fuzzy adaptive states observer is constructed to estimate the unknown states. Combined with adaptive backstepping technique, an adaptive fuzzy output feedback control method is proposed. The proposed control method ensures that all signals in the closed-loop system are bounded, and that the tracking error converges to a bounded tight set without violating the full state constraint. The simulation results prove the effectiveness of the proposed control scheme.

     

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    • References(40)
  • [1]
    T. Li, R. Zhao, C. L. P. Chen, L. Fang, and C. Liu, “Finite-time formation control of under-actuated ships using nonlinear sliding mode control,” IEEE Trans. Cybernetics, vol. 48, no. 11, pp. 3243–3253, 2018.
    [2]
    G. Y. Chen, M. Gan, C. L. P. Chen, and H. X. Li, “A regularized variable projection algorithm for separable nonlinear least-squares problems,” IEEE Trans. Automatic Control, vol. 64, no. 2, pp. 526–537, 2018.
    [3]
    C. Deng, W. N. Gao, and W. W. Che, “Distributed adaptive fault-tolerant output regulation of heterogeneous multi-agent systems with coupling uncertainties and actuator faults,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 4, pp. 1098–1106, 2020.
    [4]
    S. C. Tong, X. Min, and Y. X. Li, “Observer-based adaptive fuzzy tracking control for strict-feedback nonlinear systems with unknown control gain functions,” IEEE Trans. Cybernetics, vol. 50, no. 9, pp. 3903–3913, 2020.
    [5]
    M. Chen, S. S. Ge, and B. V. E. How, “Robust adaptive neural network control for a class of uncertain MIMO nonlinear systems with input nonlinearities,” IEEE Trans. Neural Networks and Learning Systems, vol. 21, no. 5, pp. 796–812, May 2010.
    [6]
    H. J. Yang and J. K. Liu, “An adaptive RBF neural network control method for a class of nonlinear systems,” IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 2, pp. 457–462, 2018.
    [7]
    X. Yang, C. L. P. Chen, and Y. Wang, “Optimal admission control for multi-class of wireless adaptive multimedia services,” IEICE Trans. Communications, vol. 84, no. 4, pp. 795–804, 2001.
    [8]
    Y. Xiao, C. L. P. Chen, and B. Wang, “Bandwidth degradation QoS provisioning for adaptive multimedia in wireless/mobile networks,” Computer Communications, vol. 25, no. 13, pp. 1153–1161, 2002.
    [9]
    D. Zhang, H. Yuan, and Z. C. Cao, “Environmental adaptive control of a snake-like robot with variable stiffness actuators,” IEEE/CAA Journal of Automatica Sinica, vol. 7, no. 3, pp. 745–751, 2020.
    [10]
    Q. Cui, Y. Wang, and Y. D. Song, “Neuroadaptive fault-tolerant control under multiple objective constraints with applications to tire production systems,” IEEE Trans. Neural Networks and Learning Systems, 2020.
    [11]
    J. Li, W. S. Chen, and J. M. Li, “Adaptive neural network output-feedback decentralized stabilization for a class of large scale stochastic nonlinear strict-feedback systems,” Int. Journal of Robust and Nonlinear Control, vol. 21, no. 3, pp. 452–472, 2011.
    [12]
    J. P. Yu, Y. M. Ma, H. S. Yu, and C. Lin, “Adaptive fuzzy dynamic surface control for induction motors with iron losses in electric vehicle drive systems via backstepping,” Information Sciences, vol. 376, no. 10, pp. 172–189, 2017.
    [13]
    Z. Liu, C. Chen, Y. Zhang, and C. L. P. Chen, “Adaptive neural control for dual-arm coordination of humanoid robot with unknown nonlinearities in output mechanism,” IEEE Trans. Cybernetics, vol. 45, no. 3, pp. 521–532, 2015.
    [14]
    C. L. P. Chen, G. X. Wen, Y. J. Liu, and Z. Liu, “Observer-based adaptive backstepping consensus tracking control for high-order nonlinear semi-strict-feedback multiagent systems,” IEEE Trans. Cybernetics, vol. 46, no. 7, pp. 1591–1601, Jul. 2016.
    [15]
    D. P. Li, D. J. Li, Y. J. Liu, S. Tong, and C. L. P. Chen, “Approximation-based adaptive neural tracking control of nonlinear MIMO unknown time-varying delay systems with full state constraints,” IEEE Trans. Cybernetics, vol. 47, no. 10, pp. 3100–3109, 2017.
    [16]
    W. He, S. Zhang, and S. S. Ge, “Adaptive control of a flexible crane system with the boundary output constraint,” IEEE Trans. Industrial Electronics, vol. 61, no. 8, pp. 4126–4133, Aug. 2014.
    [17]
    W. He, Y. H. Chen, and Z. Yin, “Adaptive neural network control of an uncertain robot with full state constraints,” IEEE Trans. Cybernetics, vol. 46, no. 3, pp. 620–629, Mar. 2016.
    [18]
    L. Sun, W. Huo, and Z. Jiao, “Adaptive backstepping control of spacecraft rendezvous and proximity operations with input saturation and full-state constraint,” IEEE Trans. Industrial Electronics, vol. 64, no. 1, pp. 480–492, Jan. 2017.
    [19]
    C. L. P. Chen and Y. H. Pao, “An integration of neural network and rule-based systems for design and planning of mechanical assemblies,” IEEE Trans. Systems,Man,and Cybernetics, vol. 23, no. 5, pp. 1359–1371, 1993.
    [20]
    D. Mayne, J. Rawlings, C. Rao, and P. Scokaert, “Constrained model predictive control: Stability and optimality,” Automatica, vol. 36, no. 6, pp. 789–814, Jun. 2000.
    [21]
    D. Dehaan and M. Guay, “Extremum-seeking control of state constrained nonlinear systems,” IFAC Proceedings Volumes, vol. 37, no. 13, pp. 663–668, Sep. 2004.
    [22]
    Y. M. Li, S. C. Tong, L. Liu, and G. Feng, “Adaptive output-feedback control design with prescribed performance for switched nonlinear systems,” Automatica, vol. 80, pp. 225–231, Jun. 2017.
    [23]
    K. P. Tee, S. S. Ge, and E. H. Tay, “Barrier Lyapunov Functions for the control of output-constrained nonlinear systems,” Automatica, vol. 45, no. 4, pp. 918–927, 2009.
    [24]
    D. P. Li, Y. J. Liu, S. C. Tong, C. L. P. Chen, and D. J. Li, “Neural networks-based adaptive control for nonlinear state constrained systems with input delay,” IEEE Trans. Cybernetics, vol. 49, no. 4, pp. 1249–1258, Apr. 2019.
    [25]
    Y. J. Liu, M. Gong, S. Tong, C. L. P. Chen, and D. J. Li, “Adaptive fuzzy output feedback control for a class of nonlinear systems with full state constraints,” IEEE Trans. Fuzzy Systems, vol. 26, no. 5, pp. 2607–2617, Oct. 2018.
    [26]
    K. Zhao, Y. D. Song, and Z. Zhang, “Tracking control of MIMO nonlinear systems under full state constraints: A Single-parameter adaptation approach free from feasibility conditions,” Automatica, vol. 107, pp. 52–60, 2019.
    [27]
    Z. Liu, G. Y. Lai, Y. Zhang, and C. L. P. Chen, “Adaptive neural output feedback control of output constrained nonlinear systems with unknown output nonlinearity,” IEEE Trans. Neural Networks and Learning Systems, vol. 26, no. 8, pp. 1789–1802, Aug. 2015.
    [28]
    D. P. Li, L. Liu, Y. J. Liu, S. C. Tong, and C. L. P. Chen, “Adaptive NN control without feasibility conditions for nonlinear state constrained stochastic systems with unknown time delays,” IEEE Trans. Cybernetics, vol. 49, no. 12, pp. 4485–4494, Dec. 2019.
    [29]
    T. Gao, Y. J. Liu, L. Liu and D. Li, “Adaptive neural network-based control for a class of nonlinear pure-feedback systems with time-varying full state constraints,” IEEE/CAA Journal of Automatica Sinica, vol. 5, no. 5, pp. 923–933, 2018.
    [30]
    Y. J. Liu, S. M. Lu, D. J. Li, and S. C. Tong, “Adaptive controller design-based ABLF for a class of nonlinear time-varying state constraint systems,” IEEE Trans. Systems,Man,and Cybernetics:Systems, vol. 47, no. 7, pp. 1546–1563, Jul. 2017.
    [31]
    K. P. Tee, B. Ren, and S. S. Ge, “Control of nonlinear systems with timevarying output constraints,” Automatica, vol. 47, no. 11, pp. 2511–2516, Nov. 2011.
    [32]
    S. S. Ge, C. C. Hang, and T. Zhang, “A direct adaptive controller for dynamic systems with a class of nonlinear parameterizations,” Automatica, vol. 35, no. 4, pp. 741–747, 1999.
    [33]
    Y. J. Liu, S. Tong, C. L. P. Chen, D. J. Li, “Adaptive NN control using integral barrier Lyapunov functionals for uncertain nonlinear block-triangular constraint systems,” IEEE Trans. Cybernetics, vol. 47, no. 11, pp. 3747–3757, 2016.
    [34]
    L. Liu, Y. J. Liu, S. C. Tong, and C. L. P. Chen, “Integral barrier Lyapunov function based adaptive control for switched nonlinear systems,” SCIENCE CHINA Information Sciences, vol. 63, no. 3, pp. 132203:1–132203:14, March. 2020.
    [35]
    Z. L. Tang, “Adaptive neural network control of uncertain state constrained nonlinear systems,” IFAC Proceedings Volumes, vol. 47, no. 3, pp. 2279–2284, 2014.
    [36]
    A. Boulkroune, M. Tadjine, M. M’Saad, and M. Farza, “How to design a fuzzy adaptive controller based on observers for uncertain affine nonlinear systems,” Fuzzy Sets and Systems, vol. 159, no. 8, pp. 926–948, 2008.
    [37]
    J. H. Park and S. H. Kim, “Direct adaptive output feedback fuzzy controller for a nonaffine nonlinear system,” IEE Proceedings-Control Theory and Applications, vol. 151, no. 1, pp. 65–72, 2004.
    [38]
    S. C. Tong, B. Chen, and Y. F. Wang, “Fuzzy adaptive output feedback control for MIMO nonlinear systems,” Fuzzy Sets and Systems, vol. 156, no. 2, pp. 285–299, 2005.
    [39]
    S. C. Tong, C. Y. Li, and Y. M. Li, “Fuzzy adaptive observer backstepping control for MIMO nonlinear systems,” Fuzzy Sets and Systems, vol. 160, no. 19, pp. 2755–2775, 2009.
    [40]
    L. X. Wang, “Stable adaptive fuzzy control of nonlinear systems,” IEEE Trans. Fuzzy Systems, vol. 1, no. 2, pp. 146–155, May 1993.
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    Highlights

    • So as to handle the full state constraints better, a new iBLF is constructed in this paper. By adopting iBLF, not only is the problem of full state constraints solved, but also the state is directly constrained, which avoids the known conservative condition of the transition error.
    • The fuzzy adaptive observer is constructed to deal with the unknown states of nonlinear system. What is more, combining iBLF with backstepping methods, and using the properties of fuzzy basis functions and bounded control design technology, an adaptive fuzzy output feedback control based on observer is proposed.
    • The adaptive output feedback strategy proposed in this paper can not only ensure that the signal in the closed-loop system is bounded, but also the observer error and tracking error converge to zero in a small neighborhood. Simulation results prove the effectiveness of the proposed control scheme.

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