Optimal train control via switched system dynamic optimization
This paper considers an optimal train control problem with two challenging, non-standard constraints: a speed constraint that is piecewise-constant with respect to the train's position, and control constraints that are non-smooth functions of the train's speed. We formulate this problem as...
| Main Authors: | , , , |
|---|---|
| Format: | Journal Article |
| Language: | English |
| Published: |
TAYLOR & FRANCIS LTD
2021
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/89486 |
| _version_ | 1848765230282178560 |
|---|---|
| author | Zhong, W. Lin, Qun Loxton, Ryan Lay Teo, Kok |
| author_facet | Zhong, W. Lin, Qun Loxton, Ryan Lay Teo, Kok |
| author_sort | Zhong, W. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper considers an optimal train control problem with two challenging, non-standard constraints: a speed constraint that is piecewise-constant with respect to the train's position, and control constraints that are non-smooth functions of the train's speed. We formulate this problem as an optimal switching control problem in which the mode switching times are decision variables to be optimized, and the track gradient and speed limit in each mode are constant. Then, using control parameterization and time-scaling techniques, we approximate the switching control problem by a finite-dimensional optimization problem, which is still subject to the challenging speed limit constraint (imposed continuously during each mode) and the non-smooth control constraints. We show that the speed constraint can be transformed into a finite number of point constraints. We also show that the non-smooth control constraints can be approximated by a sequence of conventional (smooth) inequality constraints. The resulting approximate problem can be viewed as a nonlinear programming problem and solved using gradient-based optimization algorithms, where the gradients of the cost and constraint functions are computed via the sensitivity method. A case study using data for a real subway line shows that the proposed method yields a realistic optimal control profile without the undesirable control fluctuations that can occur with the pseudospectral method. |
| first_indexed | 2025-11-14T11:31:57Z |
| format | Journal Article |
| id | curtin-20.500.11937-89486 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:31:57Z |
| publishDate | 2021 |
| publisher | TAYLOR & FRANCIS LTD |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-894862023-07-26T05:44:12Z Optimal train control via switched system dynamic optimization Zhong, W. Lin, Qun Loxton, Ryan Lay Teo, Kok Science & Technology Technology Physical Sciences Computer Science, Software Engineering Operations Research & Management Science Mathematics, Applied Computer Science Mathematics Optimal train control switched system control parameterization time-scaling transformation state-dependent control constraint This paper considers an optimal train control problem with two challenging, non-standard constraints: a speed constraint that is piecewise-constant with respect to the train's position, and control constraints that are non-smooth functions of the train's speed. We formulate this problem as an optimal switching control problem in which the mode switching times are decision variables to be optimized, and the track gradient and speed limit in each mode are constant. Then, using control parameterization and time-scaling techniques, we approximate the switching control problem by a finite-dimensional optimization problem, which is still subject to the challenging speed limit constraint (imposed continuously during each mode) and the non-smooth control constraints. We show that the speed constraint can be transformed into a finite number of point constraints. We also show that the non-smooth control constraints can be approximated by a sequence of conventional (smooth) inequality constraints. The resulting approximate problem can be viewed as a nonlinear programming problem and solved using gradient-based optimization algorithms, where the gradients of the cost and constraint functions are computed via the sensitivity method. A case study using data for a real subway line shows that the proposed method yields a realistic optimal control profile without the undesirable control fluctuations that can occur with the pseudospectral method. 2021 Journal Article http://hdl.handle.net/20.500.11937/89486 10.1080/10556788.2019.1604704 English TAYLOR & FRANCIS LTD fulltext |
| spellingShingle | Science & Technology Technology Physical Sciences Computer Science, Software Engineering Operations Research & Management Science Mathematics, Applied Computer Science Mathematics Optimal train control switched system control parameterization time-scaling transformation state-dependent control constraint Zhong, W. Lin, Qun Loxton, Ryan Lay Teo, Kok Optimal train control via switched system dynamic optimization |
| title | Optimal train control via switched system dynamic optimization |
| title_full | Optimal train control via switched system dynamic optimization |
| title_fullStr | Optimal train control via switched system dynamic optimization |
| title_full_unstemmed | Optimal train control via switched system dynamic optimization |
| title_short | Optimal train control via switched system dynamic optimization |
| title_sort | optimal train control via switched system dynamic optimization |
| topic | Science & Technology Technology Physical Sciences Computer Science, Software Engineering Operations Research & Management Science Mathematics, Applied Computer Science Mathematics Optimal train control switched system control parameterization time-scaling transformation state-dependent control constraint |
| url | http://hdl.handle.net/20.500.11937/89486 |