Research on the pneumatic-electric braking collaborative control strategy for heavy-haul trains based on heuristic dual-end optimization
The pneumatic-electric braking operation of heavy-haul trains on long and steep downgrade sections is fundamentally an optimal control problem, involving nonlinear state constraints, control constraints, and long-distance end constraints. Traditional collaborative control strategies for pneumatic-el...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Drive for Locomotives
2025-01-01
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| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2025.01.014 |
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| Summary: | The pneumatic-electric braking operation of heavy-haul trains on long and steep downgrade sections is fundamentally an optimal control problem, involving nonlinear state constraints, control constraints, and long-distance end constraints. Traditional collaborative control strategies for pneumatic-electric braking often struggle to simultaneously address these constraints, making it difficult to ensure the safety and stability of heavy-haul trains during the pneumatic-electric braking coordinative control process. To tackle this issue, this paper proposes a pneumatic-electric braking collaborative control strategy for heavy-haul trains based on heuristic dual-end optimization. Firstly, the dynamic characteristics of heavy-haul trains were analyzed, leading to the extraction of nonlinear state constraints, control constraints, and end constraints in the pneumatic-electric braking coordinative control process. Next, a gradient search was performed focusing on the stability objective within the nonlinear state constraints and control constraints, which resulted in the formation of a finite set sequence based on the position domain. At the same time, a neural network was designed to identify parameters for a nonlinear pneumatic braking model, thereby yielding predicted pneumatic braking forces. Subsequently, an A * algorithm was developed for iterative refinement within the finite set sequence, collaborative control sequence that satisfies the end constraints. Finally, the effectiveness of the proposed pneumatic-electric collaborative control strategy for heavy-haul trains was verified through a semi-physical simulation platform. |
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| ISSN: | 1000-128X |