Handling Delays in Yaw Rate Control of Electric Vehicles Using Model Predictive Control With Experimental Verification
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a new approach is proposed to deal with the delay in vehicle stability control using model predictive control (MPC). The vehicle considered here is a rear-wheel drive electric (RWD) vehicle. The yaw rate response of the vehicle is modified by means of torque vectoring so that it tracks the desired yaw rate. Presence of delays in a control loop can severely degrade controller performance and even cause instability. The common approaches for handling delays are often complex in design and tuning or require an increase in the dimensions of the controller. The proposed method is easy to implement and does not entail complex design or tuning process. Moreover, it does not increase the complexity of the controller; therefore, the amount of online computation is not appreciably affected. The effectiveness of the proposed method is verified by means of carsim/simulink simulations as well as experiments with a rear-wheel drive electric sport utility vehicle (SUV). The simulation results indicate that the proposed method can significantly reduce the adverse effect of the delays in the control loop. Experimental tests with the same vehicle also point to the effectiveness of this technique. Although this method is applied to a vehicle stability control, it is not specific to a certain class of problems and can be easily applied to a wide range of model predictive control problems with known delays.
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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) | 0.001 | 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 it