Data-driven Acceleration of MPC with Guarantees
📰 ArXiv cs.AI
Learn how to accelerate Model Predictive Control (MPC) using a data-driven framework with guarantees, enabling low-latency applications
Action Steps
- Construct an upper bound on the optimal cost-to-go using offline MPC solutions
- Build a nonparametric policy using the constructed upper bound
- Replace online optimization with the nonparametric policy
- Implement the policy as a nonparametric lookup rule
- Test the accelerated MPC framework in a low-latency application
Who Needs to Know This
Control engineers and researchers working on optimal control systems can benefit from this framework to improve the performance of MPC in low-latency applications. This can be particularly useful in fields like robotics, autonomous vehicles, and process control.
Key Insight
💡 Data-driven acceleration of MPC can replace online optimization with a nonparametric policy, enabling low-latency applications
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🚀 Accelerate Model Predictive Control (MPC) with a data-driven framework and guarantees! 🤖
Key Takeaways
Learn how to accelerate Model Predictive Control (MPC) using a data-driven framework with guarantees, enabling low-latency applications
Full Article
Title: Data-driven Acceleration of MPC with Guarantees
Abstract:
arXiv:2511.13588v2 Announce Type: replace-cross Abstract: Model Predictive Control (MPC) is a powerful framework for optimal control but can be too slow for low-latency applications. We present a data-driven framework to accelerate MPC by replacing online optimization with a nonparametric policy constructed from offline MPC solutions. Our policy is greedy with respect to a constructed upper bound on the optimal cost-to-go, and can be implemented as a nonparametric lookup rule that is orders of m
Abstract:
arXiv:2511.13588v2 Announce Type: replace-cross Abstract: Model Predictive Control (MPC) is a powerful framework for optimal control but can be too slow for low-latency applications. We present a data-driven framework to accelerate MPC by replacing online optimization with a nonparametric policy constructed from offline MPC solutions. Our policy is greedy with respect to a constructed upper bound on the optimal cost-to-go, and can be implemented as a nonparametric lookup rule that is orders of m
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