Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.03354 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- Model Predictive Control (MPC) faces computational demands and performance degradation from model inaccuracies. We propose two architectures combining Neural Network-approximated MPC (NNMPC) with Reinforcement Learning (RL). The first, Warm Start RL, initializes the RL actor with pre-trained NNMPC weights. The second, RLMPC, uses RL to generate corrective residuals for NNMPC outputs. We introduce a downsampling method reducing NNMPC input dimensions while maintaining performance. Evaluated on a rotary inverted pendulum, both architectures demonstrate runtime reductions exceeding 99% compared to traditional MPC while improving tracking performance under model uncertainties, with RL+MPC achieving 11-40% cost reduction depending on reference amplitude.