Stability of Receding Horizon Control with Smooth Value Functions
Bibliographic record
Abstract
Receding Horizon Control (RHC) is a very effective control methodology which has been employed in an extensive range of industrial applications. However, most of the stability results involve terminal costs or constraints which are sometimes not computationally desirable. In this work, it is shown that the smoothness of the value function is sufficient to ensure stability for control affine systems under RHC laws with no terminal cost or constraint. In order to find the infimum for all stabilizing horizons, an ODE problem based on the linearized system is developed that provides the set of stabilizing and destabilizing horizons. It is shown that the infimum of stabilizing horizons can be estimated without the need to solve the nonlinear optimal control problem, and that subject to certain conditions the exact infimum can be obtained. Simulations are provided to illustrate the application of these methods to some nonlinear systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".