MPCGPU：基于GPU上预条件共轭梯度法的实时非线性模型预测控制
MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPU
AI总结：
本文提出GPU加速的实时NMPC求解器MPCGPU，其核心为预条件共轭梯度线性系统求解器，在Kuka IIWA机械臂跟踪任务中实现千赫兹控制率并支持512个轨迹节点，求解性能平均比CPU方法快3.6倍。
摘要：
非线性模型预测控制（NMPC）是一种用于运动与操作的最先进方法，它在每个控制步骤中利用轨迹优化。尽管该方法的性能受计算能力限制，但使用迭代方法求解底层中等规模且稀疏的线性系统的直接轨迹优化实现，天然适合并行硬件加速。在这项工作中，我们介绍了MPCGPU，一种GPU加速的实时NMPC求解器，其核心利用加速的预条件共轭梯度（PCG）线性系统求解器。我们表明，MPCGPU提高了NMPC的可扩展性和实时性能，能够以更快的速率求解更大的问题。特别是，对于使用Kuka IIWA机械臂的跟踪任务，MPCGPU能够扩展到千赫兹控制速率，轨迹长度可达512个节点。这得益于一个定制的PCG求解器，该求解器在大多数求解中比最先进的基于CPU的线性系统求解器性能至少高出10倍，平均高出3.6倍。
英文摘要：
Nonlinear Model Predictive Control (NMPC) is a state-of-the-art approach for locomotion and manipulation which leverages trajectory optimization at each control step. While the performance of this approach is computationally bounded, implementations of direct trajectory optimization that use iterative methods to solve the underlying moderately-large and sparse linear systems, are a natural fit for parallel hardware acceleration. In this work, we introduce MPCGPU, a GPU-accelerated, real-time NMPC solver that leverages an accelerated preconditioned conjugate gradient (PCG) linear system solver at its core. We show that MPCGPU increases the scalability and real-time performance of NMPC, solving larger problems, at faster rates. In particular, for tracking tasks using the Kuka IIWA manipulator, MPCGPU is able to scale to kilohertz control rates with trajectories as long as 512 knot points. This is driven by a custom PCG solver which outperforms state-of-the-art, CPU-based, linear system solvers by at least 10x for a majority of solves and 3.6x on average.
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