A network-centric input-output feedback linearization-based control strategy for unmanned systems
Bibliographic record
Abstract
Large scale unmanned networks consisting of a number of heterogeneous nodes that may be configured in ad-hoc fashions and incorporating complicated architectures result in challenging problems for design of appropriate control and resource allocation optimization techniques. The problem is further compounded by the fact that designing appropriate network control methodologies subject to bandwidth, latencies and computational resources for these network-centric systems are highly non-trivial. In this paper, we only investigate one of a number of critical issues that are of interest in this domain, namely the problem of congestion control of a network that consists of three nodes that can be configured into different architectures. This study shows that depending on the interconnections between the network nodes the dynamics of the resulting closed-loop system can change considerably so that the unmanned system could become even unstable and unmanageable. Therefore, a robust control strategy is required to be able to cope with any configuration changes and to be able to address the resource allocation problem subject to the propagation delays and latencies. For sake of comparative evaluation, we first implement a standard PID control scheme which is shown to lack sufficient capability for achieving the desired performance requirements. Subsequently, a nonlinear control scheme is proposed to resolve the limitation of sensitivity of the closed-loop system to propagation delays. The proposed strategy is based on a well-known input-output feedback linearization approach that is shown to achieve an appreciable improvement in the performance of the closed-loop unmanned network and which is also less sensitive to the network propagation delays.
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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 | 0.000 |
| 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) | 0.001 | 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".