Fault detection in trajectory tracking of wheeled mobile robots
Bibliographic record
Abstract
The problem of fault detection in nonlinear systems with application to trajectory tracking of nonholonomic wheeled mobile robots (WMRs) is addressed in this thesis. For the considered application, a nonholonomic wheeled mobile robot--having nonlinear kinematics--is required to follow a predefined smooth trajectory (in the absence of obstacles in the environment). This goal has to be accomplished despite the presence of faults that may occur in two of its major subsystems which are vital for navigation, namely the driving subsystem and the steering subsystem. These faults are assumed to be caused by actuator faults in either of these two subsystems. The problem addressed here is to detect the presence of faults and to determine the subsystem which has been affected by these faults. Toward this end, two different fault detection approaches are proposed and investigated. The first approach is based on system identification through Extended Kalman Filters (EKF) whereas the second one is based on system identification via artificial neural networks. In the former approach a novel method for residual generation is proposed while in the latter by utilizing the neural network's formal stability properties the desired performance can be guaranteed. Each of the proposed fault detection methods is studied subject to two different kinds of controllers (namely a dynamic linear controller and a dynamic feedback linearization based controller) and two different types of actuator faults (namely the Loss-of-Effectiveness fault and Locked-In-Place fault). In this way, the impact of the controller strategy on the fault detection approach is also investigated and evaluated.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".