A Model-Free Leader-Follower Approach with Multi-Level Reference Command Generators
Bibliographic record
Abstract
This paper introduces an innovative approach to addressing leader-follower control challenges through iterative learning techniques. In this scheme, both the leader and the follower are guided by independent reference generators, simultaneously influencing both entities. The follower is directed by a combination of trajectories, incorporating both the leader's output and its own command generator. The interaction between leader and follower dynamics is captured through a performance index that integrates model-following errors from both entities, thereby shaping the leader's control strategy. Conversely, the follower's performance measure focuses exclusively on its local model-following errors to formulate its control strategy. This method aims to overcome limitations observed in conventional iterative learning control methods, particularly by offering causal strategies based on model-following error dynamics and by explicitly accommodating reference command signals. Notably, this development is achieved within a model-free, data-driven framework, eliminating the need for prior knowledge about the leader-follower system dynamics. Furthermore, the proposed strategies demonstrate flexibility regarding the order of model-following error dynamics. This solution is validated using a heterogeneous system of vehicles characterized by state and control signal delays.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".