A Novel Coordination Method for an Integrated Chassis Controller of an 8x8 Combat Vehicle
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The objective of the Integrated Chassis Controllers (ICC) is to combine multiple actuators and dynamics controllers to maximize the overall vehicle performance at all driving conditions. It is well known that there are two methods that can be used to develop an ICC. The first is a centralized method, where all the actuators are considered in one controller to ensure a harmonic integration between different actuators. The second method is called decentralized integration, where each actuator is considered in a separate controller and a low-level controller is used to coordinate the operation of the controllers. In this paper, the second method is used to develop a decentralized ICC using a novel controller coordinator based on Genetic Programming (GP). The GP is used to integrate torque vectoring and active rear steering controllers of an 8x8 combat vehicle. The controller is utilized to enhance the lateral stability of the vehicle in various driving conditions. The GP-based method showed a drastic reduction in the processing time in comparison to a Neuro-Fuzzy based controller. In addition, the vehicle stability is evaluated in comparison with the uncontrolled vehicle by conducting slalom maneuvers on a dry and slippery road.</div></div>
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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 | 0.002 | 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 | 0.001 |
| 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".