Robust AISMC-neural network observer-based control of high-speed autonomous vehicles with unknown dynamics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Substantial challenges still exist in designing path-tracking control systems for autonomous vehicles, particularly at speed limits or under varying operating conditions. Such problems arise for various reasons, such as the nonlinear characteristics of vehicular components, system-component interactions, constraints on the states and control inputs, and more. This paper focuses on designing a robust adaptive control system for high-speed autonomous vehicles in case the system dynamics are unknown or unavailable. For this purpose, an intelligent NN-based estimation system’s universal approximation potential will be leveraged, coupled to an adaptive integral sliding mode controller (AISMC). Unlike previously reported studies, the present paper considers the entire dynamics of the autonomous vehicle unknown rather than solely a part of the system or external disturbances merely. The Lyapunov stability theorem is employed to guarantee the asymptotic stability of the developed framework and to obtain the adaptation laws. A critical maneuver explores the effectiveness and robustness of the suggested framework under severe disturbances, parametric uncertainties, and high speeds. The obtained results indicate that the developed framework holds the capacity to navigate the vehicle alongside the desired trajectory and outperforms other reported studies in the literature subject to various external disturbances.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it