A gradient algorithm for optimal control problems with model-reality differences

Sie Long Kek; Mohd Ismail Abd Aziz; Kok Lay Teo

doi:10.3934/naco.2015.5.251

Article Contents

2015, Volume 5, Issue 3: 251-266. Doi: 10.3934/naco.2015.5.251

This issue Previous Article A quasi-Newton trust region method based on a new fractional model Next Article Matrix group monotonicity using a dominance notion

A gradient algorithm for optimal control problems with model-reality differences

1.
Department of Mathematics, Universiti Tun Hussein Onn Malaysia, 86400 Parit Raja
2.
Department of Mathematics, Universiti Teknologi Malaysia, 81310 UTM, Skudai
3.
Department of Mathematics and Statistics, Curtin University, Perth, W.A. 6845

Received: May 2014

Revised: March 2015

Published: July 2015

Abstract Related Papers Cited by

Abstract

In this paper, we propose a computational approach to solve a model-based optimal control problem. Our aim is to obtain the optimal solution of the nonlinear optimal control problem. Since the structures of both problems are different, only solving the model-based optimal control problem will not give the optimal solution of the nonlinear optimal control problem. In our approach, the adjusted parameters are added into the model used so as the differences between the real plant and the model can be measured. On this basis, an expanded optimal control problem is introduced, where system optimization and parameter estimation are integrated interactively. The Hamiltonian function, which adjoins the cost function, the state equation and the additional constraints, is defined. By applying the calculus of variation, a set of the necessary optimality conditions, which defines modified model-based optimal control problem, parameter estimation problem and computation of modifiers, is then derived. To obtain the optimal solution, the modified model-based optimal control problem is converted in a nonlinear programming problem through the canonical formulation, where the gradient formulation can be made. During the iterative procedure, the control sequences are generated as the admissible control law of the model used, together with the corresponding state sequences. Consequently, the optimal solution is updated repeatedly by the adjusted parameters. At the end of iteration, the converged solution approaches to the correct optimal solution of the original optimal control problem in spite of model-reality differences. For illustration, two examples are studied and the results show the efficiency of the approach proposed.

Keywords:

Mathematics Subject Classification: Primary: 93C05, 93C10; Secondary: 93B40.

Citation:

Related Papers

Cited by

References

[1]	V. M. Becerra and P. D. Roberts, Dynamic integrated system optimization and parameter estimation for discrete time optimal control of nonlinear systems, Int. J. Control, 63 (1996), 257-281.doi: 10.1080/00207179608921843.
[2]	A. E. Bryson and Y. C. Ho, Applied Optimal Control, Hemisphere Publishing Company, New York, 1975.
[3]	S. L. Kek, Nonlinear programming approach for optimal control problems, Proceeding of the 2nd International Conference on Global Optimization and Its Applications, (2013), 20-25.
[4]	D. E. Kirk, Optimal Control Theory: An Introduction, Mineola, NY: Dover Publications, 2004.
[5]	F. L. Lewis and V. L. Syrmos, Optimal Control, 2nd ed, John Wiley & Sons, 1995.
[6]	Q. Lin, R. Loxton and K. L. Teo, The control parameterization method for nonlinear optimal control: a survey, Journal of Industrial and Management Optimization, 10 (2014), 275-309.doi: 10.3934/jimo.2014.10.275.
[7]	R. Loxton, K. L. Teo and V. Rehbock, Computational method for a class of switched system optimal control problems, IEEE Transactions on Automatic Control, 54 (2009), 2455-2460.doi: 10.1109/TAC.2009.2029310.
[8]	R. Loxton, K. L. Teo, V. Rehbock and K. F. C. Yiu, Optimal control problems with a continuous inequality constraint on the state and the control, Automatica, 45 (2009), 2250-2257.doi: 10.1016/j.automatica.2009.05.029.
[9]	L. F. Lupián and J. R. Rabadán-Martin, LQR control methods for trajectory execution in omnidirectional mobile robots, Recent Advances in Mobile Robotics, (2011), 385-400.
[10]	L. H. Nguyen, S. Park, A. Turnip and K. S. Hong, Application of LQR control theory to the design of modified skyhook control gains for semi-active suspension systems, Proceeding of ICROS-SICE International Joint Conference, (2009), 4698-4703.
[11]	P. D. Roberts and T. W. C. Williams, On an algorithm for combined system optimization and parameter estimation, Automatica, 17 (1981), 199-209.doi: 10.1016/0005-1098(81)90095-9.
[12]	P. D. Roberts, Optimal control of nonlinear systems with model-reality differences, Proceedings of the 31st IEEE Conference on Decision and Control, 1 (1992), 257-258.
[13]	R. C. H. del Rosario and R. C. Smith, LQR control of shell vibrations via piezocreramic actuators, NASA Contractor Report 201673, ICASE Report No. 97-19, 1997.
[14]	J. Saak and P. Benner, Application of LQR techniques to the adaptive control of quasilinear parabolic PDEs, Proceedings in Applied Mathematics and Mechanics, 2007.
[15]	K. L. Teo, C. J. Goh and K. H. Wong, A Unified Computational Approach to Optimal Control Problem, Longman Scientific and Technical, Essex, 1991.
[16]	L. X. Wang, A Course in Fuzzy Systems and Control, Upper Saddle River, NJ, 1997.
[17]	C. Z. Wu, K. L. Teo and V. Rehbock, Optimal control of piecewise affine systems with piecewise affine state feedback, Journal of Industrial and Management Optimization, 5 (2009), 737-747.doi: 10.3934/jimo.2009.5.737.
[18]	B. Yang and B. Xiong, Application of LQR techniques to the anti-sway controller of overhead crane, Advanced Material Research, 139-141 (2010), 1933-1936.