Discrete-time nonlinear stochastic optimal control problem based on stochastic approximation approach
In this paper, a computational approach is proposed for solving the discrete-time nonlinear optimal control problem, which is disturbed by a sequence of random noises. Because of the exact solution of such optimal control problem is impossible to be obtained, estimating the state dynamics is current...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Scientific Research Publishing
2018
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/72308/1/Discrete-time%20nonlinear%20stochastic%20optimal%20control%20problem%20based%20on%20stochastic%20approximation%20approach.pdf |
| _version_ | 1825950063462973440 |
|---|---|
| author | Kek, Sie Long Sim, Sy Yi Leong, Wah June Teo, Kok Lay |
| author_facet | Kek, Sie Long Sim, Sy Yi Leong, Wah June Teo, Kok Lay |
| author_sort | Kek, Sie Long |
| collection | UPM |
| description | In this paper, a computational approach is proposed for solving the discrete-time nonlinear optimal control problem, which is disturbed by a sequence of random noises. Because of the exact solution of such optimal control problem is impossible to be obtained, estimating the state dynamics is currently required. Here, it is assumed that the output can be measured from the real plant process. In our approach, the state mean propagation is applied in order to construct a linear model-based optimalcontrol problem, where the model output is measureable. On this basis, an output error, which takes into account the differences between the real output and the model output, is defined. Then, this output error is minimized by applying the stochastic approximation approach. During the computation procedure, the stochastic gradient is established, so as the optimal solution of the model used can be updated iteratively. Once the convergence is achieved, the iterative solution approximates to the true optimal solution of the original optimal control problem, in spite of model-reality differences. For illustration, an example on a continuous stirred-tank reactor problem is studied, and the result obtained shows the applicability of the approach proposed. Hence, the efficiency of the approach proposed is highly recommended. |
| first_indexed | 2024-03-06T10:08:53Z |
| format | Article |
| id | upm.eprints-72308 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T10:08:53Z |
| publishDate | 2018 |
| publisher | Scientific Research Publishing |
| record_format | dspace |
| spelling | upm.eprints-723082020-05-04T00:26:28Z http://psasir.upm.edu.my/id/eprint/72308/ Discrete-time nonlinear stochastic optimal control problem based on stochastic approximation approach Kek, Sie Long Sim, Sy Yi Leong, Wah June Teo, Kok Lay In this paper, a computational approach is proposed for solving the discrete-time nonlinear optimal control problem, which is disturbed by a sequence of random noises. Because of the exact solution of such optimal control problem is impossible to be obtained, estimating the state dynamics is currently required. Here, it is assumed that the output can be measured from the real plant process. In our approach, the state mean propagation is applied in order to construct a linear model-based optimalcontrol problem, where the model output is measureable. On this basis, an output error, which takes into account the differences between the real output and the model output, is defined. Then, this output error is minimized by applying the stochastic approximation approach. During the computation procedure, the stochastic gradient is established, so as the optimal solution of the model used can be updated iteratively. Once the convergence is achieved, the iterative solution approximates to the true optimal solution of the original optimal control problem, in spite of model-reality differences. For illustration, an example on a continuous stirred-tank reactor problem is studied, and the result obtained shows the applicability of the approach proposed. Hence, the efficiency of the approach proposed is highly recommended. Scientific Research Publishing 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/72308/1/Discrete-time%20nonlinear%20stochastic%20optimal%20control%20problem%20based%20on%20stochastic%20approximation%20approach.pdf Kek, Sie Long and Sim, Sy Yi and Leong, Wah June and Teo, Kok Lay (2018) Discrete-time nonlinear stochastic optimal control problem based on stochastic approximation approach. Advances in Pure Mathematics, 8 (3). 232 - 244. ISSN 2160-0368; ESSN: 2160-0384 https://theadl.com/detail.php?id=218&vol=1 10.4236/apm.2018.83012 |
| spellingShingle | Kek, Sie Long Sim, Sy Yi Leong, Wah June Teo, Kok Lay Discrete-time nonlinear stochastic optimal control problem based on stochastic approximation approach |
| title | Discrete-time nonlinear stochastic optimal control problem based on stochastic approximation approach |
| title_full | Discrete-time nonlinear stochastic optimal control problem based on stochastic approximation approach |
| title_fullStr | Discrete-time nonlinear stochastic optimal control problem based on stochastic approximation approach |
| title_full_unstemmed | Discrete-time nonlinear stochastic optimal control problem based on stochastic approximation approach |
| title_short | Discrete-time nonlinear stochastic optimal control problem based on stochastic approximation approach |
| title_sort | discrete time nonlinear stochastic optimal control problem based on stochastic approximation approach |
| url | http://psasir.upm.edu.my/id/eprint/72308/1/Discrete-time%20nonlinear%20stochastic%20optimal%20control%20problem%20based%20on%20stochastic%20approximation%20approach.pdf |
| work_keys_str_mv | AT keksielong discretetimenonlinearstochasticoptimalcontrolproblembasedonstochasticapproximationapproach AT simsyyi discretetimenonlinearstochasticoptimalcontrolproblembasedonstochasticapproximationapproach AT leongwahjune discretetimenonlinearstochasticoptimalcontrolproblembasedonstochasticapproximationapproach AT teokoklay discretetimenonlinearstochasticoptimalcontrolproblembasedonstochasticapproximationapproach |