Parallel Distributed Compensation /H∞ Control of Lane-keeping System Based on the Takagi-Sugeno Fuzzy Model
DOI: 10.1186/s10033-020-00477-9
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Summary
This paper addresses the limitations of existing lane-keeping assist systems (LKAS), which often neglect the influence of driver behavior and external resistance forces, such as tire adhesion and aligning torque, on tracking accuracy. To reduce lateral vehicle deviation, the authors propose a robust lane-keeping control method based on a Takagi-Sugeno (T-S) fuzzy model. The approach integrates a driver model utilizing near and far visual angles and accounts for longitudinal nonlinear velocity variations to create a closed-loop driver-road-vehicle model. The methodology employs a hierarchical control structure comprising decision, control, and execution layers. The decision layer uses machine vision for lane recognition and identifies driver status via steering signal and torque thresholds to determine control authority. The control layer designs a robust $H_\infty$ controller using parallel distributed compensation (PDC) and linear matrix inequality (LMI) to calculate the expected assist torque. Additionally, a feedforward compensation controller is implemented to counteract external resistance during steering. The electric power steering (EPS) system serves as the actuator, executing active steering corrections. The vehicle-road dynamics are modeled with state variables including lateral offset, yaw angle, and steering angle, while the driver model incorporates visual compensation, neuromuscular delays, and reaction times. Validation was conducted through Carsim/Simulink co-simulations and real-vehicle tests. The results demonstrate that the proposed method effectively maintains the vehicle within the lane centerline, ensuring driving safety. By incorporating the driver’s visual perception and compensating for external resistive torques, the system reduces tracking delays and improves lateral control precision compared to methods that assume constant longitudinal velocity or ignore driver interaction. The significance of this work lies in its comprehensive modeling of the driver-vehicle-road interaction, specifically addressing the nonlinear effects of varying longitudinal speeds and external resistance. The integration of a T-S fuzzy model with $H_\infty$ control provides a robust framework for handling uncertainties in vehicle dynamics and driver behavior. This approach enhances the reliability of LKAS by ensuring that assistive interventions are coordinated with driver intent and physically accurate regarding road-tire interactions, offering a more realistic and effective solution for advanced driver assistance systems.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | DOAJ | — | — | 1 | 2026-06-25 |
| archive | success | unpaywall | — | — | 1 | 2026-06-26 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-25 |
| chunk | success | chunk | — | — | 1 | 2026-06-25 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-25 |
| promote | success | — | — | — | 1 | 2026-06-25 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-25 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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- Theoretical Contribution: computational model