Adaptive Synthesized Fault-Tolerant Autonomous Ground Vehicle Control With Guaranteed Performance and Saturated Input
Bibliographic record
Abstract
This paper investigates the synthesized motion control (simultaneous control of the path tracking and roll dynamics) of autonomous ground vehicles considering the tracking performance enhancement, actuator failures and input saturations. A novel adaptive fault-tolerant control (FTC) strategy is proposed in this study to achieve the control objective, which incorporates two contributions: Firstly, a finite-time prescribed performance function (PPF) is proposed for the prescribed performance control (PPC) based on a simplified error transformation formulation, which is used to embody the desired performance specifications and make PPC control design less complex but able to guarantee the tracking error constrained in a prescribed region within a finite time; Secondly, a stabilizing PPC controller is synthesized based on a modified Nussbaum-type function and barrier Lyapunov function (BLF), which also encompasses a simplified adaptive neural network (ANN) term for approximating the unknown system nonlinearities. The synthesized FTC controller is capable of realizing the finite-time prescribed transient control while handling the actuator failures and input saturations simultaneously. The results of a high-fidelity Simulink-CarSim simulation on a slippery road have validated the effectiveness and advantage of the proposed synthesized FTC strategy compared with a traditional sliding mode control (SMC), and a hardware-in-the-loop (HIL) simulation has been implemented to verify the real-time performance of the proposed control strategy.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".