real-time-optimal-control-shallow-recurrent-decoder

name	real-time-optimal-control-shallow-recurrent-decoder
description	Real-time optimal control framework using SHallow REcurrent Decoder networks-based Reduced Order Modeling (SHRED-ROM) for high-dimensional and parametric dynamical systems. Enables synthesis of closed-loop controllers from limited state sensor readings with effective distributed control actions in new scenarios.
metadata	{"arxiv_id":"2607.19302","published":"2026-07-21","authors":["Matteo Tomasetto","Francesco Braghin","J. Nathan Kutz","Andrea Manzoni"],"subjects":["Machine Learning (cs.LG)","Optimization and Control (math.OC)"],"tags":["SHRED-ROM","real-time-control","recurrent-decoder","reduced-order-modeling","optimal-control","systems-engineering"]}
license	Complete terms in LICENSE.txt

Real-time Optimal Control with Shallow Recurrent Decoder Networks

Overview

This methodology exploits SHallow REcurrent Decoder networks-based Reduced Order Modeling (SHRED-ROM) to synthesize real-time closed-loop controllers for high-dimensional and parametric dynamics, relying solely on limited state sensor readings.

Core Methodology

Problem Context

Controlling dynamical systems in real-time across multiple scenarios is critical for adaptive control strategies, ensuring stability and efficiency. Traditional optimal control problems require several system simulations, which are computationally demanding due to high-dimensionality of underlying spatio-temporal dynamics.

Solution Approach

SHRED-ROM synthesizes real-time closed-loop controllers by:

1. Training on a few optimal examples from an expert demonstrator
2. Mimicking expert behavior with effective distributed control actions in new scenarios
3. Alleviating the curse of dimensionality through reduced-order modeling
4. Synthesizing a sensor forecaster to close the loop at the latent level

Key Components

1. Expert Demonstrator Examples: Limited optimal control examples serve as training data
2. SHRED-ROM Architecture: Shallow recurrent decoder networks for reduced-order modeling
3. Distributed Control Actions: Effective control distribution across system parameters
4. Sensor Forecaster: Mitigates sensor failures or delays by closing the loop at latent level
5. Latent-Level Control: Operates in reduced-dimensional latent space for efficiency

Implementation Workflow

Step 1: Expert Data Collection

• Gather optimal control examples from expert demonstrator
• Focus on diverse scenarios covering expected operational range
• Include both standard and edge-case operating conditions

Step 2: SHRED-ROM Training

• Implement shallow recurrent decoder network architecture
• Train model to mimic expert behavior from limited examples
• Validate generalization to unseen scenarios

Step 3: Sensor Forecaster Synthesis

• Develop forecaster to predict sensor readings at latent level
• Integrate forecaster into control loop for robustness
• Test under various sensor failure/delay conditions

Step 4: Real-time Deployment

• Deploy trained SHRED-ROM controller for real-time operation
• Monitor performance across different scenarios
• Implement fallback mechanisms for extreme conditions

Step 5: Performance Assessment

• Evaluate on challenging high-dimensional cases
• Test parametric density control scenarios
• Validate fluid flow control performance
• Compare computational efficiency vs. traditional approaches

Applications

• Parametric Density Control: Adaptive control for systems with varying density parameters
• Fluid Flow Control: Real-time optimization of complex fluid dynamics
• High-dimensional Systems: Control of systems with many degrees of freedom
• Limited Sensor Scenarios: Operation with minimal sensor infrastructure
• Real-time Adaptive Control: Rapid response to changing system conditions

Pitfalls and Considerations

Training Data Limitations

• Quality and diversity of expert examples directly impact generalization
• Insufficient coverage of operational scenarios leads to poor performance

Model Architecture Design

• Balance between model depth and real-time computational requirements
• Shallow architecture must capture essential dynamics without overfitting

Sensor Integration

• Sensor forecaster effectiveness depends on available sensor modalities
• Latent-level control requires careful design of observation mapping

Validation Complexity

• High-dimensional test cases require significant computational resources
• Multiple scenario validation is essential for robustness assessment

Activation Keywords

• SHRED-ROM
• shallow recurrent decoder
• real-time optimal control
• reduced order modeling
• latent-level control
• sensor forecaster
• distributed control actions
• systems engineering control

References

• Original Paper: arXiv:2607.19302 [cs.LG, math.OC]
• Related Work: Reduced-order modeling for control systems
• Implementation: SHallow REcurrent Decoder networks architecture