Bibliographic record
Abstract
Unmanned vehicles (UVs) are playing an increasingly significant role in modern daily life. In the past decades, numerous commercial, scientific, and military communities across the world are developing fully autonomous UVs for a variety of applications, such as environmental monitoring and surveillance, post-disaster search and rescue, border patrol, natural resources exploration, and experimental platforms for new technologies verification. The excessive opportunities and threats that come along with these diverse applications have created a niche demand for UVs to extend their capabilities to perform more sophisticated and hazardous missions with greater autonomy, lower costs of development and operation, improved personnel safety and security, extended operational range (reliability) and precision, as well as increased flexibility in sophisticated environments including so-called dirty, dull, harsh, and dangerous missions. \n \nIn order to successfully and effectively execute missions and meet their corresponding performance criteria and overcome these ever-increasing challenges, greater autonomy together with more advanced reliable and safe motion control systems are required to offer the critical technologies for ensuring intelligent, safe, reliable, and efficient control of UVs in the presence of disturbances, actuator saturation, and even actuator faults, especially for practical applications. \n \nThis thesis concentrates on the development of different reliable and safe motion control algorithms/strategies applicable to UVs, in particular, unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs). A number of contributions pertaining to the fault detection and diagnosis (FDD), fault-tolerant control (FTC), disturbance estimation and compensation, and actuator saturation avoidance have been made in this thesis. In addition to the control problems, this thesis also presents several guidance-related contributions, including adaptive observer-based line-of-sight (LOS) guidance law, time-varying lookahead distance scheme, piecewise path switching criterion for guiding a single UV, as well as a proportional-integral (PI) type of leader-follower formation guidance strategy for a group of UVs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".