Active Front Wheel Steering System using Yaw Rate Estimation based Fuzzy Logic Due to Various Lateral Wind Disturbance
DOI: 10.15282/ijame.19.2.2022.17.0759
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Summary
This study addresses the challenge of maintaining lateral vehicle stability under lateral wind disturbances, a common external factor that jeopardizes passenger safety. While Active Front Wheel Steering (AFWS) systems are effective for yaw rate control, previous research often relied on simplified linear vehicle models or constant disturbance inputs, primarily focusing on armored vehicles rather than passenger cars. To address these gaps, the authors developed and compared three control strategies—Proportional-Integral-Derivative (PID), fuzzy-tuned PID, and fuzzy logic controllers—for an AFWS system applied to a nonlinear 9-degree-of-freedom (9-DOF) passenger vehicle model. The research methodology involved deriving a comprehensive mathematical vehicle model incorporating subsystems for load distribution, the Pacejka tire model, handling dynamics, and a 2-DOF rack-and-pinion steering mechanism with an electric power-assisted steering (EPAS) actuator. This model was verified against data from IPG CarMaker simulation software at a longitudinal speed of 80 km/h. A key innovation was the derivation of a yaw rate observer that estimates desired yaw rate without compromising the nonlinearity of the vehicle model, enabling testing with active driver steering inputs. The controllers were evaluated under two conditions: first, with zero steering input subjected to three lateral wind disturbance profiles; second, with a double lane change (DLC) maneuver to simulate driver intervention under more extreme disturbance magnitudes. The results demonstrated that all three controllers effectively stabilized the vehicle against lateral wind disturbances. However, the fuzzy logic controller outperformed the PID and fuzzy-tuned PID variants, achieving the lowest error rates. Specifically, when utilizing the developed yaw rate observer during the DLC maneuver, the fuzzy logic controller maintained an error of less than 5%. The study confirms that the nonlinear 9-DOF model provides a more realistic assessment of vehicle dynamics compared to simplified linear models used in prior works. The significance of this work lies in its application of advanced control strategies to passenger vehicles under realistic, nonlinear conditions. By validating the fuzzy logic controller’s superior performance in rejecting lateral wind disturbances while accommodating driver steering inputs, the study provides a robust framework for enhancing AFWS systems. This contributes to the broader field of automotive safety by offering a more accurate and resilient control solution for maintaining vehicle stability in adverse environmental conditions.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-24 |
| archive | success | canonical_url | — | — | 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 | 2026-06-24 |
| 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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