Energy-optimal control of intelligent track inspection trains: design and experiment
Bibliographic record
Abstract
A new energy-efficient control strategy for intelligent track inspection trains (ITITs) was developed and its effectiveness evaluated. A sufficiently simple state-space model for a real ITIT is introduced in this paper. This model was used to formulate and solve a constrained optimisation problem to determine the vehicle's optimal speed profile using the pseudo-spectral method to achieve the highest energy savings. A model predictive control (MPC) scheme was then used to create a controller to follow the resulting optimal speed trajectory as closely as possible. The results obtained from co-simulation between a high-fidelity model of the ITIT built within the Amesim software program and the MPC scheme developed in the Matlab/Simulink software environment showed that the optimal speed trajectory was tracked very well when the MPC scheme was applied to the vehicle's high-fidelity model. During the co-simulations, the energy consumption in terms of the battery's state of charge (SOC) changes for the MPC-based optimal speed trajectory following was around 6% less than that for a conventional non-optimal cruise controller. Moreover, in an experiment with a real ITIT, the energy consumption in terms of the SOC changes for the non-optimal cruise controller was 5% more than that for the MPC-based optimal speed trajectory following.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".