用GPU加速非线性模型预测控制，毫秒级实现高维机器人系统安全鲁棒控制。
Safe Large-Scale Robust Nonlinear MPC in Milliseconds via Reachability-Constrained System Level Synthesis on the GPU
- 基于GPU并行的系统层级合成，联合优化轨迹、控制器与可达集。
- 相比顶尖CPU求解器提速97.7%，20毫秒内完成75维人形机器人控制。
- 首次在高维系统中实现100%实测安全性，适合复杂机器人实时控制场景。
我们提出GPU-SLS，一种基于GPU并行的框架，用于安全、鲁棒的非线性模型预测控制（MPC），可扩展至高维不确定机器人系统和长规划时域。该方法实时联合优化不等式约束的动态可行基准轨迹、跟踪控制器与扰动下的闭环可达集。为高效计算基准轨迹，我们开发了基于交替方向乘子法（ADMM）框架的新型GPU加速二次规划（QP）求解器，采用并行关联扫描与自适应缓存。相同的GPU QP后端用于通过系统层级合成（SLS）优化鲁棒跟踪控制器与闭环可达集，支持固定与滚动时域设置下的可达性约束控制。性能显著提升：相较先进CPU求解器，基准轨迹求解时间减少97.7%，较其他GPU求解器降低71.8%；SLS控制与可达性计算加速237倍。尽管问题规模巨大，本方法实现100%经验安全性，优于高维学习基可达性基线。我们在复杂非线性系统上验证，包括全肢体四足机器人（61维）与人形机器人（75维），在线于GPU上平均20毫秒内合成鲁棒控制策略，可处理达2×10⁵决策变量与8×10⁴约束的问题。代码开源：https://github.com/Jeff300fang/gpu_sls。
原文摘要 · Abstract (English)
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 quadratic programming procedure with a novel GPU-accelerated quadratic program (QP) solver that uses parallel associative scans and adaptive caching within an alternating direction method of multipliers (ADMM) framework. The same GPU QP backend is used to optimize robust tracking controllers and closed-loop reachable sets via system level synthesis (SLS), enabling reachability-constrained control in both fixed- and receding-horizon settings. We achieve substantial performance gains, reducing nominal trajectory solve times by 97.7% relative to state-of-the-art CPU solvers and 71.8% compared to GPU solvers, while accelerating SLS-based control and reachability by 237x. Despite large problem scales, our method achieves 100% empirical safety, unlike high-dimensional learning-based reachability baselines. We validate our approach on complex nonlinear systems, including whole-body quadrupeds (61D) and humanoids (75D), synthesizing robust control policies online on the GPU in 20 milliseconds on average and scaling to problems with 2 x 10^5 decision variables and 8 x 10^4 constraints. The implementation of our method is available at https://github.com/Jeff300fang/gpu_sls.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。