A Receding Horizon Battery Shortage Prevention Control Strategy for Electric Unmanned Vehicles
Bibliographic record
Abstract
In this paper, we deal with the reference tracking control problem for Electric Unmanned Vehicles (EUV) equipped with batteries of limited energy capacity. We design a novel control architecture, equipped with a battery manager module, which is capable of avoiding energy shortage by appropriately imposing time-varying upper bounds on the vehicle's maximum acceleration. In particular, we exploit some key properties of the Set-Theoretic Model Predictive Control (ST-MPC) paradigm to couple the reference tracking and the battery shortage problems. First, given a desired path, we off-line design a conservative maximum acceleration profile capable of assuring that the EUV will reach the desired target without incurring into a battery shortage along the path. Then, on-line, by following a receding horizon philosophy and by considering a cost function of interest, we show that the battery manager can improve the acceleration profile by using the current battery's state-of-charge. Moreover, we show that the time-varying acceleration constraints imposed by the battery manager do not affect the recursive feasibility of the used ST-MPC tracking controller. Finally, a simulation example is presented to clarify and show the potential and features of the proposed control framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".