Composite Adaptive Control for Anti-Unwinding Attitude Maneuvers: An Exponential Stability Result Without Persistent Excitation
Bibliographic record
Abstract
This paper provides an exponential stability result for the adaptive anti-unwinding attitude tracking problem of a rigid body with uncertain inertia parameters, without the need for a persistent excitation (PE) condition. Specifically, a composite adaptive control scheme with guaranteed parameter convergence is proposed by integrating the dynamic regressor extension and mixing (DREM) technique into the dynamically scaled immersion and invariance adaptive control framework, wherein we modify the scaling factor so that the algorithm design does not involve any dynamic gains. To avoid the unwinding problem, a barrier function is introduced as the attitude error function, along with the establishment of two key algebraic properties for exponential stability analysis. Aiding by an linear time-varying filter, the scalar regressor of DREM is extended to generate an exciting counterpart. In this manner, the derived controller is shown to permit closed-loop exponential stability under a strictly weaker interval excitation condition than PE, in the sense that both the output-tracking and parameter estimation errors exponentially converge to zero. Furthermore, the composite adaptive law is also augmented to achieve finite/fixed-time parameter convergence in a time-synchronized manner. Simulation results are presented to verify our theoretical findings.
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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 | 0.001 | 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 | 0.000 | 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".