Distributed Neural Observer-Based Formation Strategy of Non-Affine Nonlinear Multi-Agent Systems with Unknown Dynamics
Bibliographic record
Abstract
The state estimation in Multi-Agent Systems (MASs) is a challenging problem. This is due to the fact that (1) controlling nonaffine nonlinear MASs is a difficult task and also (2) the agents in MASs have direct impacts on each other. This paper presents a new distributed Neural Networks (NN) observer for the nonlinear dynamical model of MASs with nonaffine unknown dynamical agents. The proposed scheme uses the Backpropagation learning algorithm to estimate the unknown nonlinear functions of the agents. Compared with the previous studies, which primarily concentrated on the observer design for Multiple Input Multiple Output (MIMO) systems, the proposed method is applied to nonaffine nonlinear MASs. The advantages of this method are the overall stability, the fast convergence of the observer error to zero and the robustness against both uncertainties and disturbances. Nonlinear flexible-joint robots and nonlinear dynamic duffing chaotic systems are simulated to demonstrate the effectiveness and robustness of the proposed method. The proposed method is also compared with the Luenberger observer. The guaranteed stability, better performance in the presence of agents’ uncertainties, robustness against disturbances are the main advantages of the proposed method compared with the traditional observer.
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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) | 0.001 | 0.001 |
| 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".