HSMS-Based Event-Triggered Adaptive Dynamic Programming for Pursuit–Evasion Differential Games of Multiagent Systems
Bibliographic record
Abstract
This article investigates the distributed approximate optimal control problem for pursuit-evasion differential games (PEDGs) of multiagent systems (MASs). Initially, interactions between pursuer agents and the evader agents are formulated using a divide-and-conquer algebraic graph approach, where all agents desire to maintain cohesion with their teammates. Subsequently, a state event-triggered mechanism (ETM) is introduced to conserve communication resources. Meanwhile, a polymeric hierarchical sliding mode surface (HSMS) incorporating local neighbor errors is constructed such that the system response rate is improved. To enhance team coordination, a novel dynamic target allocation algorithm is designed to execute the rational allocation among pursuers. Furthermore, based on the adaptive dynamic programming (ADP) with a single-critic neural network (NN) architecture, the HSMS-based event-triggered optimal control policies are further designed via solving the coupling Hamilton-Jacobi-Bellman (HJB) equations. Finally, a simulation conducted in the representative two-pursuer-two-evader scenario is presented to validate the effectiveness of the proposed control scheme.
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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) | 0.002 | 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".