A new gradient computational formula for optimal control problems with time-delay

Changjun Yu; Lei Yuan; Shuxuan Su

doi:10.3934/jimo.2021076

Article Contents

2022, Volume 18, Issue 4: 2469-2482. Doi: 10.3934/jimo.2021076

This issue Previous Article Optimal

$ Z $

-eigenvalue inclusion intervals of tensors and their applications Next Article A modification of Galerkin's method for option pricing

A new gradient computational formula for optimal control problems with time-delay

Shanghai University, Shanghai, 200444, China

^* Corresponding author: Lei Yuan
^* Corresponding author: Lei Yuan

Received: November 30, 2020

Revised: January 31, 2021

Published: July 2022

This work is supported by National Natural Science Foundation of China(NSFC), Grant No.11871039 and Science and Technology Commission of Shanghai Municipality(STCSM), Grant No. 20JC1413900.

Abstract Full Text(HTML) Figure(0) / Table(3) Related Papers Cited by

Abstract

In this paper, we consider a class of time-delay optimal control problem (TDOCP) with canonical equality and inequality constraints. By applying control parameterization method together with time-scaling transformation, a TDOCP can be readily solved by gradient-based optimization methods. The partial derivative of the cost as well as the constraint functions with respect to the decision variables are obtained by variational approach, which is inefficient when the discretization for the control function is relatively dense. For general optimal control problem without time-delay, co-state approach is an effective way to compute the gradients, however, when time-delay is involved in the dynamic system, the co-state system is not known. In this paper, we derive the co-state system for TDOCP to compute the gradients of the cost and constraints. Numerical results show that the computational efficiency is much higher when compared with the traditional variational approach.

Keywords:

Mathematics Subject Classification: Primary: 90C30, 90-08; Secondary: 34H05.

Citation:

FullText(HTML)

Table 3. Experimental results of co-state method and variational method

Problem	numbers of partitions	co-state method CPU time(s)	variational method CPU time(s)	optimal cost
Prob 1	p=4	9.62	11.98	1.7500
	p=6	18.04	29.49	1.7407
	p=8	26.14	31.73	1.7405
	p=12	61.41	117.46	1.7403
Prob 2	p=5	10.2	147.23	2.4046
Prob 3	p=10	183	2702	2.0356
Prob 4	p=1	1.180	1.729	0.0218
	p=3	4.485	22.39	0.0176
	p=6	12.92	329.01	0.0142
Prob 5	p=5	8.72	51.56	2.1502

| Show Table

DownLoad: CSV

Table 1. Parameters in Problem 2

a	b	c	$ t_f $	Q	R	S
0.2	0.5	0.2	1.5	$ I_{2\times2} $	$ I_{2\times2} $	$ 10^4I_{2\times2} $

| Show Table

DownLoad: CSV

Table 2. Parameters in Problemk 5

a	b	c	h	$ t_f $	Q	R	S
0.2	0.5	0.2	1	2	$ I_{2\times2} $	$ I_{2\times2} $	$ 10^4I_{2\times2} $

| Show Table

DownLoad: CSV

Related Papers

Cited by

References

[1]	J. T. Betts, S. L. Campbell and K. C. Thompson, Optimal control software for constrained nonlinear systems with delays, in IEEE International Symposium on Computer-Aided Control System Design (CACSD), IEEE, 2011,444–449.
[2]	Q. Chai, R. Loxton, K. L. Teo and C. Yang, A class of optimal state-delay control problems, Nonlinear Analysis: Real World Applications, 14 (2013), 1536-1550. doi: 10.1016/j.nonrwa.2012.10.017.
[3]	Q. Chai, R. Loxton, K. L. Teo and C. Yang, A unified parameter identification method for nonlinear time-delay systems, Journal of Industrial and Management Optimization, 9 (2013), 471-486. doi: 10.3934/jimo.2013.9.471.
[4]	Q. Q. Chai, C. H. Yang, K. L. Teo and W. H. Gui, Optimal control of an industrial-scale evaporation process: Sodium aluminate solution, Control Engineering Practice, 20 (2012), 618-628. doi: 10.1016/j.conengprac.2012.03.001.
[5]	L. Denis-Vidal, C. Jauberthie and G. Joly-Blanchard, Identifiability of a nonlinear delayed-differential aerospace model, IEEE Transactions on Automatic Control, 51 (2006), 154-158. doi: 10.1109/TAC.2005.861700.
[6]	V. Deshmukh, H. Ma and E. A. Butcher, Optimal control of parametrically excited linear delay differential systems via chebyshev polynomials, Optimal Control Applications and Methods, 27 (2006), 123-136. doi: 10.1002/oca.769.
[7]	C. J. Goh and K. L. Teo, Control parametrization: A unified approach to optimal control problems with general constraints, Automatica, 24 (1988), 3-18. doi: 10.1016/0005-1098(88)90003-9.
[8]	L. Göllmann, D. Kern and H. Maurer, Optimal control problems with delays in state and control variables subject to mixed control–state constraints, Optimal Control Applications and Methods, 30 (2009), 341-365. doi: 10.1002/oca.843.
[9]	L. Göllmann and H. Maurer, Theory and applications of optimal control problems with multiple time-delays, Journal of Industrial and Management Optimization, 10 (2014), 413-441. doi: 10.3934/jimo.2014.10.413.
[10]	K. Kaji and K. H. Wong, Nonlinearly constrained time-delayed optimal control problems, Journal of Optimization Theory and Applications, 82 (1994), 295-313. doi: 10.1007/BF02191855.
[11]	C. Y. Kaya and J. L. Noakes, Computational method for time-optimal switching control, Journal of Optimization Theory and Applications, 117 (2003), 69-92. doi: 10.1023/A:1023600422807.
[12]	H. W. J. Lee, K. L. Teo, V. Rehbock and L. S. Jennings, Control parametrization enhancing technique for time optimal control problems, Dynamic Systems and Applications, 6 (1997), 243-261.
[13]	H. W. J. Lee, K. L. Teo, V. Rehbock and L. S. Jennings, Control parametrization enhancing technique for optimal discrete-valued control problems, Automatica, 35 (1999), 1401-1407. doi: 10.1016/S0005-1098(99)00050-3.
[14]	B. Li, C. J. Yu, K. L. Teo and G. R. Duan, An exact penalty function method for continuous inequality constrained optimal control problem, Journal of Optimization Theory and Applications, 151 (2011), 260-291. doi: 10.1007/s10957-011-9904-5.
[15]	L. Li, C. Yu, N. Zhang, Y. Bai and Z. Gao, A time-scaling technique for time-delay switched systems, Discrete and Continuous Dynamical Systems-S, 13 (2020), 1825-1843. doi: 10.3934/dcdss.2020108.
[16]	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.
[17]	Q. Lin, R. Loxton, K. L. Teo and Y. H. Wu, A new computational method for optimizing nonlinear impulsive systems, Dynamics of Continuous, Discrete and Impulsive Systems B, 18 (2011), 59-76.
[18]	R. C. Loxton, K. L. Teo and V. Rehbock, Optimal control problems with multiple characteristic time points in the objective and constraints, Automatica, 44 (2008), 2923-2929. doi: 10.1016/j.automatica.2008.04.011.
[19]	M. Malek-Zavarei and M. Jamshidi, Time-Delay Systems: Analysis, Optimization and Applications, North-Holland Systems and Control Series, 9. North-Holland Publishing Co., Amsterdam, 1987.
[20]	H. R. Marzban and S. M. Hoseini, An efficient discretization scheme for solving nonlinear optimal control problems with multiple time delays, Optimal Control Applications and Methods, 37 (2016), 682-707. doi: 10.1002/oca.2187.
[21]	P. Mu, L. Wang and C. Liu, A control parameterization method to solve the fractional-order optimal control problem, Journal of Optimization Theory and Applications, 187 (2020), 234-247. doi: 10.1007/s10957-017-1163-7.
[22]	A. Nasir, E. M. Atkins and I. Kolmanovsky, Robust science-optimal spacecraft control for circular orbit missions, IEEE Transactions on Systems Man and Cybernetics Systems, 50 (2020), 923-934. doi: 10.1109/TSMC.2017.2767077.
[23]	J. Nocedal and S. J. Wright, Numerical Optimization, 2$^nd$ edition, Springer Series in Operations Research and Financial Engineering, Springer, New York, 2006.
[24]	D. Stefanatos, Optimal shortcuts to adiabaticity for a quantum piston, Automatica, 49 (2013), 3079-3083. doi: 10.1016/j.automatica.2013.07.020.
[25]	R. F. Stengel, R. Ghigliazza, N. Kulkarni and O. Laplace, Optimal control of innate immune response, Optimal Control Applications and Methods, 23 (2002), 91-104. doi: 10.1002/oca.704.
[26]	K. L. Teo, C. J. Goh and K. H. Wong, A Unified Computational Approach to Optimal Control Problems, Longman Scientific and Technical, 1991.
[27]	T. L. Vincent, W. J. Grantham and W. Stadler, Optimality in Parametric Systems, American Society of Mechanical Engineers Digital Collection, 1983.
[28]	L. Wang, J. Yuan, C. Wu and X. Wang, Practical algorithm for stochastic optimal control problem about microbial fermentation in batch culture, Optimization Letters, 13 (2019), 527-541. doi: 10.1007/s11590-017-1220-z.
[29]	K. H. Wong, L. S. Jennings and F. Benyah, The control parametrization enhancing transform for constrained time–delayed optimal control problems, ANZIAM Journal, 43 (2001), 154-185.
[30]	D. Wu, Y. Bai and F. Xie, Time-scaling transformation for optimal control problem with time-varying delay, Discrete and Continuous Dynamical Systems-S, 13 (2020), 1683-1695. doi: 10.3934/dcdss.2020098.
[31]	D. Wu, Y. Bai and C. Yu, A new computational approach for optimal control problems with multiple time-delay, Automatica, 101 (2019), 388-395. doi: 10.1016/j.automatica.2018.12.036.
[32]	X. Xu and P. J. Antsaklis, Optimal control of switched systems based on parameterization of the switching instants, IEEE Transactions on Automatic Control, 49 (2004), 2-16. doi: 10.1109/TAC.2003.821417.
[33]	C. Yu, B. Li, R. Loxton and K. L. Teo, Optimal discrete-valued control computation, Journal of Global Optimization, 56 (2013), 503-518. doi: 10.1007/s10898-012-9858-7.
[34]	C. Yu, Q. Lin, R. Loxton, K. L. Teo and G. Wang, A hybrid time-scaling transformation for time-delay optimal control problems, Journal of Optimization Theory and Applications, 169 (2016), 876-901. doi: 10.1007/s10957-015-0783-z.
[35]	N. Zhang, C. -J. Yu and F. -S. Xie, The time-scaling transformation technique for optimal control problems with time-varying time-delay switched systems, Journal of the Operations Research Society of China, 8 (2020), 581-600. doi: 10.1007/s40305-020-00299-5.