\`x^2+y_1+z_12^34\`
Advanced Search
Article Contents
Article Contents

A hybrid parametrization approach for a class of nonlinear optimal control problems

Abstract Full Text(HTML) Figure(8) / Table(9) Related Papers Cited by
  • In this paper, a suitable hybrid iterative scheme for solving a class of non-linear optimal control problems (NOCPs) is proposed. The technique is based upon homotopy analysis and parametrization methods. Actually an appropriate parametrization of control is applied and state variables are computed using homotopy analysis method (HAM). Then performance index is transformed by replacing new control and state variables. The results obtained from the given method are compared with the results which are obtained using the spectral homotopy analysis method (SHAM), homotopy perturbation method (HPM), optimal homotopy perturbation method (OHPM), modified variational iteration method (MVIM) and differential transformations. The existence and uniqueness of the solution are presented. The comparison and ability of the given approach is illustrated via two examples.

    Mathematics Subject Classification: 49J22, 49M30.

    Citation:

    \begin{equation} \\ \end{equation}
  • 加载中
  • Figure 1.  Approximate solution of $ x_1(t) $ and $ u_1(t) $ for (m = 4, k = 2)

    Figure 2.  Approximate solution of x2(t) and u2(t) for (m = 4, k = 2)

    Figure 3.  Approximate solution of x3(t) and u3(t) for (m = 4, k = 2)

    Figure 4.  h-curve at 4-order of approximation of $ x_1(t) $ and $ x_2(t) $

    Figure 5.  h-curve at 4-order of approximation of $ x_3(t) $

    Figure 6.  Approximate solution of $ x_1(t) $ and $ x_2(t) $ for (m = 7, k = 3)

    Figure 7.  Approximate solution of $ u(t) $

    Figure 8.  h-curve at 7-order of approximation of x1(t) and x2(t)

    Table 1.  Minimum of performance index value $ J_k $ of the proposed method

    Itr CPU time (sec.) HAM and parametrization approaches
    m=4, k=1 $ 0.109 $ $ 0.00468778 $
    m=4, k=2 $ 0.121 $ $ 0.00468778 $
     | Show Table
    DownLoad: CSV

    Table 2.  The Max error of the proposed method for $ x_1(t) $ that $ k = 2 $ and $ h = -1 $ in comparison to SHAM and HPM

    Method CPU time (sec.) Max error
    proposed method (m=4, k=2) $ 0.155 $ $ 2.93152 * 10^{-17} $
    SHAM (Legendre) (m=6, N=50, h=-1.2) $ 0.224 $ $ 1.0589* 10^{-9} $
    SHAM (Chebyshev) (m=6, N=50, h=-1.2) $ 0.224 $ $ 1.0586* 10^{-9} $
    HPM (m=6) $ 46.401 $ $ 3.1420* 10^{-8} $
     | Show Table
    DownLoad: CSV

    Table 3.  Minimum of performance index value $ J $ of the proposed method and other methods

    Method Cost function CPU time (sec.)
    Proposed Method (m=4, k=2, h=-1) $ 0.00468778 $ $ 0.141 $
    SHAM Chebyshev (m=6, N=50, h=-1.2) $ 0.0046877944625923 $ $ 0.226 $
    SHAM Legendre (m=6, N=50, h=-1.2) $ 0.0046877944625906 $ $ 0.227 $
    HPM (m=3) $ 0.004687795533 $ $ 10.821 $
    OHPM (m=1) $ 0.004688009428 $ $ - $
    MVIM (m=3) $ 0.004687986656 $ $ - $
     | Show Table
    DownLoad: CSV

    Table 4.  Minimum of performance index value $ J_k $ of the proposed method

    Itr CPU time (sec.) HAM and parametrization approaches
    m=7, k=1 $ 0.016 $ $ 1.07504 $
    m=7, k=2 $ 0.031 $ $ 1.0136 $
    m= 7, k=3 $ 0.032 $ $ 1.01184 $
     | Show Table
    DownLoad: CSV

    Table 5.  The Max error of our method of $ x_1(t) $ in comparison to SHAM and HPM

    Itr Max error
    Proposed Method (m=7, k=3, h=-0.9) $ 3.16673\times10^{-5} $
    SHAM Chebyshev (m=15, N=50, h=-0.5) $ 4.2749\times10^{-4} $
    SHAM Legendre (m=15, N=50, h=-0.5) $ 4.2749\times10^{-4} $
    DT (m=15) $ 4.4380\times10^{-4} $
     | Show Table
    DownLoad: CSV

    Table 6.  Minimum of performance index value J of the proposed method and other methods

    Method Cost function CPU time (sec.)
    Proposed Method (m=7, k=3, h=-0.9) $ 1.01184 $ $ 0.032 $
    SHAM Chebyshev (m=15, N=50, h=-0.5) $ 1.0472 $ $ 0.200 $
    SHAM Legendre (m=15, N=50, h=-0.5) $ 1.0472 $ $ 0.188 $
    DT (m=15) $ 1.0478 $ $ 87.74 $
     | Show Table
    DownLoad: CSV

    Table 7.  Minimum of performance index value Jk of the proposed method

    Itr CPU time (sec.) HAM and parametrization approaches
    m=7, k=1 0.016 1.07504
    m=7, k=2 0.031 1.0136
    m= 7, k=3 0.032 1.01184
     | Show Table
    DownLoad: CSV

    Table 8.  The Max error of our method of x1(t) in comparison to SHAM and HPM

    Itr Max error
    Proposed Method (m=7, k=3, h=-0.9) $5.36319\times10^{-5}$
    SHAM Chebyshev (m=15, N=50, h=-0.5) $4.2749\times10^{-4}$
    SHAM Legendre (m=15, N=50, h=-0.5) $4.2749\times10^{-4}$
    DT (m=15) $4.4380\times10^{-4}$
     | Show Table
    DownLoad: CSV

    Table 9.  Minimum of performance index value J of the proposed method and other methods

    Method Cost function
    Proposed Method (m=4, k=2) 1.04483
    SHAM Chebyshev (m=15, N=50, h=-0.5) 1.0472
    SHAM Legendre (m=15, N=50, h=-0.5) 1.0472
    DT (m=15) 1.0478
     | Show Table
    DownLoad: CSV
  • [1] S. Abbasbandi, Homotopy analysis method for Kawahara equations nonlinear analysis, Real World Applications, 11 (2010), 307-312.  doi: 10.1016/j.nonrwa.2008.11.005.
    [2] S. EffatiH. Saberi Nik and M. Shirazian, Analytic-approximate solution for a class of nonlinear optimal control problems by homotopy analysis method, Asian-European Journal of Mathematics, 6 (2013), 1-22.  doi: 10.1142/S1793557113500125.
    [3] S. Ganjefar and S. Rezaei, Modified homotopy perturbation method for optimal control problems using Pade approximant, Applied Mathematical Modelling, 40 (2016), 7062-7081.  doi: 10.1016/j.apm.2016.02.039.
    [4] X. GaoK. L. Teo and G. R. Duan, An optimal control approach to spacecraft rendezvous on elliptical orbit, Optim. Control Appl. Meth., 36 (2015), 158-178.  doi: 10.1002/oca.2108.
    [5] C. K. Ghaddar, Rapid solution of optimal control problems by a functional spreadsheet paradigm: A practical method for the non-programmer, Mathematical and Computational Applications, 23 (2018), 54-82.  doi: 10.3390/mca23040054.
    [6] C. J. Goh and K. L. Teo, Control parameterization: a unified approach to optimal control problem with general constraints, Automatica, 24 (1988), 3-18.  doi: 10.1016/0005-1098(88)90003-9.
    [7] Q. GongI. M. RossW. Kang and F. Fahroo, Connections between the covector mapping theorem and convergence of pseudospectral methods for optimal control, Comput. Optim. Appl., 41 (2008), 307-335.  doi: 10.1007/s10589-007-9102-4.
    [8] J. H. He, Homotopy perturbation method: A new nonlinear analytical technique, Appl. Math. Comput., 135 (2003), 73-79.  doi: 10.1016/S0096-3003(01)00312-5.
    [9] I. Hwang, A computational approach to solve optimal control problems using differential transformation, In Proceedings of the 2007 American Control Conference, Marriott Marquis Hotel at Times Square, New York City, USA, 11–13, July 2007.
    [10] M. ItikM. U. Salamci and S. P. Banksa, Optimal control of drug therapy in cancer treatment, Nonlinear Analysis, 71 (2009), 1473-1486. 
    [11] H. Jafari and M. Alipour, Solution of the Davey Stewartson equation using homotopy analysis method, Nonlinear Analysis: Modelling and Control, 15 (2010), 423-433. 
    [12] A. JajarmiN. ParizA. Vahidian Kamyad and S. Effati, A highly computational efficient method to solve nonlinear optimal control problems, Scientia Iranica D, 19 (2012), 759-766. 
    [13] A. JajarmiM. HajipourE. Mohammadzadeh and Du mitru Baleanu, A new approach for the nonlinear fractional optimal control problems with external persistent disturbances, Journal of the Franklin Institute, 355 (2018), 3938-3967.  doi: 10.1016/j.jfranklin.2018.03.012.
    [14] W. JiaX. He and L. Guo, The optimal homotopy analysis method for solving linear optimal control problems, Applied Mathematical Modelling, 45 (2017), 865-880.  doi: 10.1016/j.apm.2017.01.024.
    [15] X. J. TangJ. L. Wei and K. Chen, A Chebyshev-Gauss pseudospectral method for solving optimal control problems, Acta Automatica Sinica, 41 (2015), 1778-1787. 
    [16] J. L. Junkins and J. D. Turner, Optimal Spacecraft Rotational Maneuvers, Elsevier-Amsterdam, 1986.
    [17] M. El-Kady, Legendre approximations for solving optimal control problems governed by ordinary differential equations, International Journal of Control Science and Engineering, 2 (2012), 54-59. 
    [18] B. KafashA. DelavarkhalafiS. M. Karbassi and K. Boubaker, A numerical approach for solving optimal control problems using the Boubaker polynomials expansion scheme, Journal of Interpolation and Approximation in Scientific Computing, 2014 (2014), 1-18.  doi: 10.5899/2014/jiasc-00033.
    [19] S. L. Kek, K. L. Teo and M. I. A. Aziz, Efficient output solution for nonlinear stochastic optimal control problem with model-reality differences, Mathematical Problems in Engineering, 2015 (2015), Article ID 659506, 9 pages. doi: 10.1155/2015/659506.
    [20] M. Keyanpour and M. Azizsefat, Numerical solution of optimal control problems by an iterative scheme, AMO- Advanced Modeling and Optimization, 13 (2011), 25-37. 
    [21] R. LiaK. L. TeoK. H. Wong and G. R. Duan, Control parameterization enhancing transform for optimal control of switched systems, Mathematical and Computer Modelling, 43 (2006), 1393-1403.  doi: 10.1016/j.mcm.2005.08.012.
    [22] S. J. Liao, The Proposed Homotopy Analysis Technique for the Solution of Nonlinear Problems, Ph.D. Thesis- Shanghai Jiao Tong University, 1992.
    [23] S. J. Liao, Beyond Perturbation: Introduction to the Homotopy Analysis Method, CRC Press-Boca Raton, Chapman Hall, 2003.
    [24] S. J. Liao, Homotopy Analysis Method in Nonlinear Differential Equations, Springer/Higher Education, 2012.
    [25] Q. LinR. LoxtonK. L. Teo and Y. H. Wu, Optimal control computation for nonlinear systems with state-dependents stopping criteria, Automatica, 48 (2012), 2116-2129.  doi: 10.1016/j.automatica.2012.06.055.
    [26] Q. LinR. Loxton and K. L. Teo, Optimal control of nonlinear switched systems: Computational methods and applications, JORC, 1 (2013), 275-311. 
    [27] Q. LinR. Loxton and K. L. Teo, The control parameterization method for nonlinear optimal control: A survey, Journal of Industrial and Managment Optimization, 10 (2014), 275-309.  doi: 10.3934/jimo.2014.10.275.
    [28] M. Matinfar and M. Saeidy, A new analytical method for solving a class of nonlinear optimal control problems, Optimal Control Applications and Methods, 35 (2014), 286-302.  doi: 10.1002/oca.2068.
    [29] H. Mirinejad and T. Inanc, An RBF collocation method for solving optimal control problems, Robotics and Autonomous Systems, 87 (2017), 219-225. 
    [30] A. NazemiS. Hesam and A. Haghbin, An application of differential transform method for solving nonlinear optimal control problems, Computational Methods for Differential Equations, 3 (2015), 200-217. 
    [31] S. Nezhadhosein, A. Heyda and R. Ghanbari, A modified hybrid genetic algorithm for solving nonlinear optimal control problems, Mathematical Problems in Engineering, 2015, Article ID 139036, 21 pages. doi: 10.1155/2015/139036.
    [32] H. Saberi NikS. EffatiS. S. Motsa and M. Shirazian, Spectral homotopy analysis method and its convergence for solving a class of nonlinear optimal control problems, Numer. Algor., 65 (2014), 171-194.  doi: 10.1007/s11075-013-9700-4.
    [33] M. Shirazian and S. Effati, Solving a class of nonlinear optimal control problems via Hes variational iteration method, International Journal of Control, Automation, and Systems, 10 (2012), 249-256. 
    [34] O. Y. Stryk and R. Bulirsch, Direct and indirect methods for trajectory optimazation, Annals of Operations Research, 37 (1992), 357-373.  doi: 10.1007/BF02071065.
    [35] K. L. Teo, C. J. Goh and K. H. Wong, A Unified Computational Approach to Optimal Control Problems, Longman Scientific and Technical, Essex, 1991.
    [36] K. L. TeoL. S. JenningsH. W. J. Lee and V. Rehbock, Control parametrization enhancing technique for constrained optimal control problems, J. Austral. Math. Soc. B, 40 (1999), 314-335.  doi: 10.1017/S0334270000010936.
    [37] S. Wei, M. Zefran and R. A. Decarlo, Optimal control of robotic system with logical constraints: application to UAV path planning, Q6 Proceeding(s) of the IEEE International Conference on Robotic and Automation, Pasadena. CA, USA, 2008.
    [38] X. S. Chen, X. K. Li, L. L. Zhang, and S. T. Cai, A new spectral method for the nonlinear optimal control, Proceedings of the 36th Chinese Control Conference, July 26–28, 2017, Dalian, China.
  • 加载中

Figures(8)

Tables(9)

SHARE

Article Metrics

HTML views(1655) PDF downloads(340) Cited by(0)

Access History

Other Articles By Authors

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return