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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".