MPCGPU: GPU上の前処理付き共役勾配法によるリアルタイム非線形モデル予測制御
MPCGPU: Real-Time Nonlinear Model Predictive Control through Preconditioned Conjugate Gradient on the GPU
GPUで加速した前処理付き共役勾配法を核に、非線形モデル予測制御をリアルタイムで解くソルバを提案し、Kuka IIWAの追従タスクで512ノット点・キロヘルツ制御を実現した。
著者: Emre Adabag, Miloni Atal, William Gerard, Brian Plancher
分類: cs.RO, cs.DC
原文アブストラクト
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.
