Secure Control of T–S Fuzzy-Based Nonlinear Active Quarter-Vehicle Suspension Systems Under Malicious Attacks With Experimental Validation
Bibliographic record
Abstract
It is challenging to ensure the safety of vehicles in the presence of malicious attacks. For this reason, the security control problem of nonlinear active quarter-vehicle suspension systems (QVSSs) is investigated in this paper. Within transportation cyber-physical systems, a specific Takagi-Sugeno fuzzy representation is adopted to capture the nonlinear behavior of vehicle dynamics. Firstly, accurately capturing the randomness and variability of attacks within complex driving environments poses a challenge. To precisely model the randomness of attacks, a probabilistically uncertain denial of service (PUDoS) attack strategy is adopted, thereby providing a practical range for potential attacks. Secondly, a novel probability-dependent homogeneous polynomial non-quadratic control law (PHNQCL) is designed. On the one hand, the high-order character of the controller enables the introduction of more groups of gain matrices, enhancing the control flexibility and reducing the conservatism. On the other hand, the designed controller exhibits strong defense capabilities against PUDoS attacks. Subsequently, feasible criteria for the PHNQCL are established using a high-order Lyapunov function, which not only stabilizes the active QVSSs but also reduces the conservatism. Finally, hardware-in-the-loop tests serve to validate the feasibility of the proposed method. Note to Practitioners—As a critical part of automotive chassis, the active quarter-vehicle suspension systems (QVSSs) are crucial for improving ride comfort and ensuring handling stability. However, in real-world environments, external disturbances, uncertainties, and nonlinearities can degrade their performance. As networks evolve, the interconnections in active QVSSs increase. This makes communication channels more vulnerable to attacks, further compromising vehicle safety. This paper explores security control for nonlinear QVSSs, modeling them using T-S fuzzy logic to capture vehicle dynamics. To simulate a complex vehicle driving environment, a probabilistically uncertain DoS attack strategy is designed. The challenge is to design new control methods to mitigate the performance degradation caused by attacks while enhancing vehicle safety in diverse scenarios. Hardware-in-the-loop tests are performed to verify that the proposed control algorithm can provide strong operational stability, ride comfort, and protection of vehicle components.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".