Certified Neural Approximation of Robust Nonlinear Model Predictive Control
To address this, we present a framework for generating neural network controllers that provably inherit the safety and stability guarantees of a robust model predictive policy.
ProofPaper ↗
Key points
- Model predictive control is a high-performance control method, but it incurs comparatively high online computational costs across many control tasks and is challenging to realize in low-level hardware settings.
- We treat the approximation error as a bounded input disturbance, which is addressed in the MPC design.
- This allows the neural network controller to directly inherit robustness from the underlying model predictive controller, which enables the transfer of guarantees to the approximation.
- We further demonstrate in simulation that the learned controller reproduces the closed-loop behavior of the original controller while reducing online computation.
Sources (1)
- [1]Certified Neural Approximation of Robust Nonlinear Model Predictive ControlarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 10:46 AM
To address this, we present a framework for generating neural network controllers that provably inherit the safety and stability guarantees of a robust model predictive policy.
Model predictive control is a high-performance control method, but it incurs comparatively high online computational costs across many control tasks and is challenging to realize in low-level hardware settings.
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