Stable Leader-Follower Systems: A Passivity Perspective for Telerobotics and Vehicle Platooning
Bibliographic record
Abstract
Advances in computing, more affordable hardware electronics, and reliable communications have led to development of cutting-edge robotic applications where two or more subsystems interact in a leader-follower arrangement to perform tasks. The major sources of instability and performance degradation in leader-follower systems are time delays and dynamic uncertainties, therefore, stability analysis tools and control architectures are developed to guarantee robust stability amid these uncertainties. In this thesis, the focus is on telerobotic and vehicle platooning examples of leader-follower systems. Telerobotic systems extend the manipulation capability of humans at different scales to remote, virtual, or dangerous environments. A strategy is developed to resolve the problems encountered in telerobotic applications with significant disparity between leader and follower workspace. Utilizing proximity of the follower from the environment, a hybrid strategy that transitions from rate to position mode is designed. Passivity theory is employed to guarantee stability, while experiments are conducted to determine the viability of the hybrid system. Energy-based methods like absolute stability are typically model-based, therefore, their outcome is heavily dependent on the accuracy of the teleoperator model. Depending on the degree of uncertainty in the dynamic model of the teleoperator and existing noise, the system may behave as potentially unstable when the model-based analysis predicts otherwise. A methodology to experimentally verify the absolute stability of leader-follower teleoperation systems is developed by utilizing data obtained from three experiments often experienced in teleoperation. The proposed method is examined against the benchmark Llewellyn's absolute stability criterion. String stability is a property of vehicle platoons which ensure the system states are not amplified downstream along the string of vehicles, causing multi-vehicle accidents. In platoons that employ constant distant spacing, linear controllers and distance from the preceding vehicle only, string stability cannot be guaranteed. A visual tool to assess string stability is presented, and a passivity-based sufficient condition for string stability in the frequency domain is developed. The control strategy, accounting for uncertainties and time delays is validated using numerical simulations. The three application examples show how stability robustness against uncertainties can be tackled employing "energy-based" methods, while meeting the systems performance objectives.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".