Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU

Overview

We present GPU-SLS, a GPU-parallelized framework for safe, robust nonlinear model predictive control (MPC) that scales to high-dimensional uncertain robotic systems and long planning horizons. Our method jointly optimizes an inequality-constrained, dynamically-feasible nominal trajectory, a tracking controller, and a closed-loop reachable set under disturbance, all in real-time. To efficiently compute nominal trajectories, we develop a sequential

Source Paper

Title: Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU
Authors: Jeffrey Fang, Glen Chou
arXiv: 2604.07644v1
Published: 2026-04-08
Categories: cs.RO, cs.AI, eess.SY, math.OC

Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU

Overview

Source Paper

Title: Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU
Authors: Jeffrey Fang, Glen Chou
arXiv: 2604.07644v1
Published: 2026-04-08
Categories: cs.RO, cs.AI, eess.SY, math.OC

Safe Large Scale Robust Nonlinear Mpc

Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU

Overview

Source Paper

Safe Large Scale Robust Nonlinear Mpc

Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU

Overview

Source Paper

Core Concepts

Key Contributions

Practical Applications

References

Activation Keywords

Tools Used

Instructions for Agents

Examples

Example 1: Basic Usage

Example 2: Advanced Usage

Continuous Learning V2

Continuous Learning V2

Continuous Learning V2

Continuous Learning

Continuous Learning

Pytorch Patterns