円錐線形二次制御のための並列動的計画法
Parallel Dynamic Programming for Conic Linear Quadratic Control
ADMMを用いて時間軸に沿ってLQ制御問題を分割し、各サブ問題を動的計画法で並列に解くことで、マルチコアCPU上で最大5倍の高速化を実現した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Luyao Zhang, Gabriel Bravo-Palacios, Brian Plancher, Sergio Grammatico
分類: math.OC, cs.RO, cs.SY, eess.SY
原文アブストラクト
Linear Quadratic (LQ) control problems are at the heart of linear control theory and Model Predictive Control (MPC). While performant, standard approaches to solving such problems are inherently serial, limiting real-time scalability despite the parallel computing power available on modern multi-core CPUs. Contributing to addressing this challenge and motivated by ``divide and conquer'' strategies, we present a parallel-in-time approach that solves computationally demanding conic optimal control problems through the use of the alternating direction method of multipliers (ADMM). In particular, we formulate the inner primal update of ADMM as an LQ problem and split the reformulated problem along the time horizon. This enables us to derive a variant of the Riccati recursion using dynamic programming to solve each subproblem in parallel. Numerical benchmarks on two real-world applications demonstrate as much as a 5x speedup compared to existing related approaches on multi-core CPU hardware.
