Finite-time Event-triggered Control for a Class of Nonlinear Systems
Bibliographic record
Abstract
This paper considers an approximate solution of the event-triggered Hamilton-Jacobi-Bellman (ET-HJB) equation to derive a finite-time suboptimal event-triggered control law for a class of nonlinear systems. To reduce the communication and computation overhead, the control law is computed and actuated after violating a predefined state-dependent event triggering condition. To obtain the controller gain, the ET-HJB equation is approximated as a state-dependent differential Riccati equation (SDRE). After converting the ET-HJB into an SDRE, a frozen time concept is used to eliminate the issues related to state dependency in the system and input matrices between two consecutive events. This helps reframe the SDRE into a simple differential Riccati equation (DRE), where the state-dependent system and input matrices remain fixed until the next event occurs. Using the solution of a differential Lyapunov equation, the solution of the DRE is computed forward in time. The designed event-triggered control law is readily amenable to an online implementation, and also it ensures the input-to-state stability of closed-loop systems. Simulation results are reported to prove the efficacy of the proposed control approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".