Distributed electric vehicle decoupling control based on GA-BP neural network
Bibliographic record
Abstract
Aiming at the coupling interference phenomenon of distributed electric vehicle in longitudinal and lateral motion, a decoupled controller using genetic algorithm optimism BP neural network (GA-BP) is proposed. The top controller is designed as GA-BP neural network decoupling controller, the decoupling linearization system is established based on the principle of neural network inverse system, the neural network is constructed and trained, the weights and thresholds of BP neural network were acquired, and the optimal value is obtained by GA algorithm. However, the lower controller is designed to take the minimum tire loading rate as the objective function, and the quadratic programming algorithm is adopted for the online optimization of the system. Co-simulation based on Carsim and MATLAB/Simulink is carried out to verify the effectiveness of the control strategy. The results show that the proposed GA-BP controller has good decoupling characteristics and achieves the effect of independent controllability of the vehicle longitudinal and lateral systems, small controllable range of the side-slip angle, and improved tracking accuracy of the yaw rate, which improves the mobility and driving stability of the vehicle.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".