American Institute of Mathematical Sciences

Advanced
April  2016, 12(2): 781-810. doi: 10.3934/jimo.2016.12.781

VISUAL MISER: An efficient user-friendly visual program for solving optimal control problems

Feng Yang 1, , Kok Lay Teo 2, , Ryan Loxton 3, , Volker Rehbock 3, , Bin Li 4, , Changjun Yu 5, and  Leslie Jennings 6,

1. 

School of Automation Engineering, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, Sichuan, 611731, China
2. 

Department of Mathematics and Statistics, Curtin University of Technology, GPO Box U 1987, Perth, W.A. 6845
3. 

Department of Mathematics and Statistics, Curtin University, GPO Box U1987 Perth, Western Australia 6845
4. 

Department of Mathematics and Statistics, Curtin University, GPO Box U1987, Perth, WA 6845
5. 

School of Business, Central South University, South Lushan Road, Changsha, Hunan, China
6. 

Department of Mathematics, University of Western Australia, Nedlands, Western Australia 6009, Australia

Received  November 2014 Revised  April 2015 Published  June 2015

The FORTRAN MISER software package has been used with great success over the past two decades to solve many practically important real world optimal control problems. However, MISER is written in FORTRAN and hence not user-friendly, requiring FORTRAN programming knowledge. To facilitate the practical application of powerful optimal control theory and techniques, this paper describes a Visual version of the MISER software, called Visual MISER. Visual MISER provides an easy-to-use interface, while retaining the computational efficiency of the original FORTRAN MISER software. The basic concepts underlying the MISER software, which include the control parameterization technique, a time scaling transform, a constraint transcription technique, and the co-state approach for gradient calculation, are described in this paper. The software structure is explained and instructions for its use are given. Finally, an example is solved using the new Visual MISER software to demonstrate its applicability.
Keywords: intel visual fortran, Optimal control, optimization., visual MISER.
Mathematics Subject Classification: Primary: 49M37, 49M25; Secondary: 65K0.
Citation: Feng Yang, Kok Lay Teo, Ryan Loxton, Volker Rehbock, Bin Li, Changjun Yu, Leslie Jennings. VISUAL MISER: An efficient user-friendly visual program for solving optimal control problems. Journal of Industrial & Management Optimization, 2016, 12 (2) : 781-810. doi: 10.3934/jimo.2016.12.781
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show all references

References:
[1]

World Scientific, Singapore, 2006. doi: 10.1142/6262.  Google Scholar

[2]

McGraw-Hill, 1966.  Google Scholar

[3]

Differential Equations and Nonlinear Mechanics, Volume 2007.  Google Scholar

[4]

Orlands, Florida, Academic Press, 1977.  Google Scholar

[5]

Hemisphere Publishing, DC, 1975.  Google Scholar

[6]

C. Buskens, NUDOCCCS, FORTRAN-Subroutine NUDOCCCS (Numerical Discretisation method for Optimal Control problems with Constraints in Controls and States),, 2010. , ().   Google Scholar

[7]

Journal of Optimization Theory and Applications, 107 (2000), 505-527. doi: 10.1023/A:1026491014283.  Google Scholar

[8]

Springer-Verlag, New York, 1983. doi: 10.1007/978-1-4613-8165-5.  Google Scholar

[9]

Control Engineering Practice, 20 (2002), 618-628. doi: 10.1016/j.conengprac.2012.03.001.  Google Scholar

[10]

Springer, The Netherlands, 2005.  Google Scholar

[11]

Chemical Engineering Science, 54 (1999), 2715-2720. doi: 10.1016/S0009-2509(98)00375-3.  Google Scholar

[12]

M. E. Fisher and L. S. Jennings, MATLAB MISER,, , (): 483.   Google Scholar

[13]

P. E. Gill, W. Murray, M. A. Saunders and M. H. Wright, User's Guide for NPSOL 5.0: Fortran package for nonlinear programming,, 1986. , ().   Google Scholar

[14]

Research Notes in Mathematics, Vol. 47, Pitman (Advance Publishing Program), London, 1981.  Google Scholar

[15]

Automatica, 24 (1988), 3-18. doi: 10.1016/0005-1098(88)90003-9.  Google Scholar

[16]

Journal of Optimization Theory and Applications, 26 (1978), 395-425. doi: 10.1007/BF00933463.  Google Scholar

[17]

Pacific Journal of Optimization, 6 (2010), 3157-3175.  Google Scholar

[18]

Annals of Operations Research, 98 (2000), 65-87. doi: 10.1023/A:1019235819716.  Google Scholar

[19]

Journal of the Franklin Institute, 339 (2002), 479-498. doi: 10.1016/S0016-0032(02)00028-5.  Google Scholar

[20]

the University of Western Australia, 2004. Google Scholar

[21]

Automatica, 26 (1990), 371-375. doi: 10.1016/0005-1098(90)90131-Z.  Google Scholar

[22]

Journal of Industrial and Management Optimization, 8 (2012), 591-609. doi: 10.3934/jimo.2012.8.591.  Google Scholar

[23]

Automatica, 48 (2012), 660-665. doi: 10.1016/j.automatica.2012.01.019.  Google Scholar

[24]

Journal of Optimization Theory and Applications, 154 (2012), 30-53. doi: 10.1007/s10957-012-0006-9.  Google Scholar

[25]

Journal of Optimization Theory and Applications, 134 (2007), 191-206. doi: 10.1007/s10957-007-9217-x.  Google Scholar

[26]

SIAM Journal on Numerical Analysis, in press, 2008. doi: 10.1137/060675034.  Google Scholar

[27]

North Holland, 1991.  Google Scholar

[28]

Journal of Optimization Theory and Applications, 49 (1986), 47-63. doi: 10.1007/BF00939247.  Google Scholar

[29]

Applied Mathematics and Computation, 224 (2013), 866-875. doi: 10.1016/j.amc.2013.08.092.  Google Scholar

[30]

Journal of Optimization Theory and Applications, 151 (2011), 260-291. doi: 10.1007/s10957-011-9904-5.  Google Scholar

[31]

Discrete and Continuous Dynamical Systems Series B, 16 (2011), 1101-1117. doi: 10.3934/dcdsb.2011.16.1101.  Google Scholar

[32]

International Journal of Innovative Computing, Information and Control, 6 (2010), 521-532. Google Scholar

[33]

Australian and New Zealand Industrial and Applied Mathematics Journal, 51 (2009), 162-177. doi: 10.1017/S1446181110000040.  Google Scholar

[34]

Acta Astronautica, 74 (2011), 131-140. doi: 10.1016/j.actaastro.2011.12.021.  Google Scholar

[35]

Cybernetics and Systems, 22 (1991), 1-16. doi: 10.1080/01969729108902267.  Google Scholar

[36]

Journal of Industrial and Management Optimization, 10 (2014), 275-309. doi: 10.3934/jimo.2014.10.275.  Google Scholar

[37]

Automatica, 45 (2009), 973-980. doi: 10.1016/j.automatica.2008.10.031.  Google Scholar

[38]

Automatica, 45 (2009), 2250-2257. doi: 10.1016/j.automatica.2009.05.029.  Google Scholar

[39]

IEEE Transactions on Automatic Control, 54 (2009), 2455-2460. doi: 10.1109/TAC.2009.2029310.  Google Scholar

[40]

Numerical Algebra, Control and Optimization, 2 (2012), 571-599. doi: 10.3934/naco.2012.2.571.  Google Scholar

[41]

Automatica, 49 (2013), 2652-2664. doi: 10.1016/j.automatica.2013.05.027.  Google Scholar

[42]

Pacific Journal of Optimization, 10 (2014), 537-560.  Google Scholar

[43]

Chapman & Hall/CRC, Boca Raton, 2000. doi: 10.1201/9781420036022.  Google Scholar

[44]

Chemical Engineering Journal, 75 (1999), 1-9. Google Scholar

[45]

World Scientific, 1994, 185pp. Google Scholar

[46]

MATLAB - The Language of Technical Computing, http://mathworks.com/products/matlab/,, 2008., ().   Google Scholar

[47]

Optimal Control Applications and Methods, 19 (1998), 185-203. doi: 10.1002/(SICI)1099-1514(199805/06)19:3<185::AID-OCA627>3.0.CO;2-E.  Google Scholar

[48]

Numerical Algorithms, 42 (2006), 165-169. doi: 10.1007/s11075-006-9035-5.  Google Scholar

[49]

Journal of Mathematical Analysis and Applications, 119 (1986), 21-54. doi: 10.1016/0022-247X(86)90142-3.  Google Scholar

[50]

Journal of Optimization Theory and Applications, 100 (1999), 1-13. Google Scholar

[51]

R. Petzold and A. C. Hindmarsh, LSODA, Ordinary Differential Equation Solver for Stiff or Non-Stiff System,, 2005., ().   Google Scholar

[52]

CRC Press, 1987. Google Scholar

[53]

Journal of Industrial and Management Optimization, 3 (2007), 331-348. doi: 10.3934/jimo.2007.3.585.  Google Scholar

[54]

Automatica, 18 (1982), 257-266. doi: 10.1016/0005-1098(82)90086-3.  Google Scholar

[55]

K. Schittkowski, NLPQLP: A new fortran implementation of a sequential quadratic programming algorithm for parallel computing,, 2010., ().   Google Scholar

[56]

A. L. Schwartz, RIOTS-A Matlab toolbox for solving general optimal control problems,, 2008. , ().   Google Scholar

[57]

Journal of Optimization Theory and Applications, 46 (1985), 265-293. doi: 10.1007/BF00939285.  Google Scholar

[58]

Springer, 2006.  Google Scholar

[59]

Longman Scientific and Technical, England, 1991.  Google Scholar

[60]

Journal of Australian Mathematical Society, Series B, 40 (1999), 314-335. doi: 10.1017/S0334270000010936.  Google Scholar

[61]

Automatica, 29 (1993), 789-792. doi: 10.1016/0005-1098(93)90076-6.  Google Scholar

[62]

Journal of Australian Mathematical Society, Series B, 33 (1992), 517-530. doi: 10.1017/S0334270000007207.  Google Scholar

[63]

IMA - Journal of Mathematical Control and Information, 6 (1989), 81-95. doi: 10.1093/imamci/6.1.81.  Google Scholar

[64]

Journal of Optimization Theory and Applications, 56 (1988), 145-156. doi: 10.1007/BF00938530.  Google Scholar

[65]

International Journal of Mechanical Sciences, 12 (1970), 973-983. doi: 10.1016/0020-7403(70)90037-8.  Google Scholar

[66]

in Proc. 2nd European Congress on Intelligent Techniques and Soft Computing (EUFIT) (H.J. Zimmermann ed.), Aachen, Germany, pp. 347-351, 1994. Google Scholar

[67]

Journal of Industrial and Management Optimization, 2 (2006), 435-450. doi: 10.3934/jimo.2006.2.435.  Google Scholar

[68]

Institute for Systems Research, University of Maryland, Technical Report SRC-TR-92-107r5, College Park, MD 20742. Google Scholar

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