モデル予測制御における零空間構造の活用
Nullspace Structure in Model Predictive Control
MPCのコストを二次近似しダイナミクスを線形化することで零空間構造を導出し、階層的タスク計画やガウスエキスパートの積としての零空間計算を可能にした研究。
著者: Hakan Girgin, Sylvain Calinon
分類: cs.RO
原文アブストラクト
Robotic tasks can be accomplished by exploiting different forms of redundancies. This work focuses on planning redundancy within Model Predictive Control (MPC) in which several paths can be considered within the MPC time horizon. We present the nullspace structure in MPC with a quadratic approximation of the cost and a linearization of the dynamics. We exploit the low rank structure of the precision matrices used in MPC (encapsulating spatiotemporal information) to perform hierarchical task planning, and show how nullspace computation can be treated as a fusion problem (computed with a product of Gaussian experts). We illustrate the approach using proof-of-concept examples with point mass objects and simulated robotics applications.
