Beyond Adaptive Control: A Control Method for Nonlinear Systems With Uncertainties, Applied to COVID-19
Bibliographic record
Abstract
When the outcome of an action cannot be precisely known, it is difficult to select actions to get a desired result. This problem can be caused by uncertain parameters, such as not knowing how slippery a road is when driving in icy conditions. Adaptive control techniques can estimate uncertainties using past measurements, but the confidence in these estimates is not used to inform future control actions. Dual control, an improvement on adaptive control, can estimate the reductions in uncertainty that will result from control actions and probes the system to identify the uncertain parameters to a sufficient level to optimize the desired goal. However, existing dual control approaches have been computationally intractable for all but the simplest of control problems. Here we show that our novel and computationally efficient dual iterative linear quadratic Gaussian controller outcompetes an adaptive iterative linear quadratic Gaussian controller, using the control of COVID-19 as an example application. The dual controller performed 6.4% better than the adaptive controller in selecting policies to minimize the social and economic costs associated with both the policies and case counts using an established model of COVID-19 with sixteen uncertain parameters. Our results demonstrate that dual control is a powerful control tool that can handle complex, nonlinear, and stochastic systems in a robust and actively adaptive way while improving their performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".