Comparative Study of Trajectory Tracking Control for Automated Ground Vehicles via Model Predictive Control and Robust H-infinity State Feedback Control
Bibliographic record
Abstract
Abstract A comparative study of longitudinal and lateral control maneuverer in model predictive control (MPC) schemes and robust state feedback control (RSC) method for trajectory tracking of automated ground vehicles (AGVs) is presented in this paper. Both MPC-based and RSC-based tracking controller are designed on the same basis of longitudinal-lateral-yaw motions of a single-track vehicle model. The main objective is to compare the controllers’ performance of tracking accuracy of path and velocity under different test scenarios. The simulation is implemented on Carsim-Simulink joint platform using high-fidelity vehicle model and the mass uncertainties, sensor measurement noise and the performance in extreme driving conditions: turn with big curvature are considered. The simulation results indicate that mass uncertainty and sensor measurement noise of lateral velocity have little effect on the RSC-based controller, while that have relatively great influence on MPC-based one. However, MPC-based controller shows a shorter response time and more accurate tracking performance than RSC-based scheme. Finally, for the test of turn with curvature 0.02 , the maximum velocity that RSC-based controller can carry out has reached 22m/s, which is slightly better than MPC-based one: 21m/s.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".