An Intelligent Parallel Distributed Compensation Control Strategy for Robust Autonomous Vehicle Path-Following: Tackling Speed Variations, Road Uncertainty, and Actuator Limits
Bibliographic record
Abstract
This paper presents an enhanced Parallel Distributed Compensation (PDC) control law for autonomous vehicle pathfollowing, modeled within the Takagi-Sugeno (T-S) fuzzy framework. Path-following control for autonomous ground vehicles (AGVs) remains a major challenge due to nonlinear dynamics, time-varying parameters, and external disturbances. Traditional control methods, such as PID controllers and Model Predictive Control (MPC), face critical limitations, including tuning complexity, high computational demands, and reliance on full-state feedback. To address these challenges, we propose an improved PDC control strategy incorporating a Quadratic Lyapunov function, ensuring system stability and robustness under real-world constraints. The key contributions of this work include: Design of a novel fuzzy-based steering controller capable of handling actuator saturation, unknown road curvature, and uncertain lateral wind forces. Integration of a relaxed stability condition via a Quadratic Lyapunov function, improving control adaptability and robustness. Extensive simulation validation at different speeds ($8 \text{km} / \mathrm{h}, 30 \text{km} / \mathrm{h}$, and$45 \text{km} / \mathrm{h}$), demonstrating enhanced trajectory tracking, stability, and disturbance rejection compared to conventional methods. Results confirm that the proposed enhanced PDC control strategy significantly improves lateral control performance while maintaining computational efficiency for real-time applications. This study highlights the feasibility and effectiveness of integrating an advanced PDC control law within a T-S fuzzy modeling framework, paving the way for more reliable and adaptive autonomous vehicle control systems.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".