Learning a Mixture of Experts Approximation of a Model Predictive Controller with Guarantees
Bibliographic record
Abstract
In this paper, we investigate the integration of a Mixture of Experts (MoE) architecture into Model Predictive Control (MPC) frameworks. The proposed approach enhances the real-time applicability and computational efficiency of MPC while guaranteeing closed-loop stability and constraint satisfaction. The MoE architecture provides a flexible, modular strategy for representing complex control policies by partitioning the input space into regions, each managed by a specialized expert model coordinated via a gating network. We evaluate the architecture’s effectiveness in alleviating the computational burden of traditional MPC and leveraging its universal function approximation capabilities. This involves developing a learning-based control policy through approximations that characterize MPC behavior with stability guarantees. The practicality of the approach is demonstrated via simulations, highlighting its potential for robust, real-time control in diverse dynamic systems. By presenting both theoretical foundations and practical implementations, this paper advances control strategies that adaptively handle computational constraints while maintaining high performance in safety-critical applications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".