適応的時間メッシュ細分化を用いた非線形モデル予測制御
Non-Linear Model Predictive Control with Adaptive Time-Mesh Refinement
時間格子を適応的に細分化することで離散化誤差を抑えつつ計算量を抑えたリアルタイムNMPCを提案し、UAVシミュレーションで検証した論文。
著者: Ciro Potena, Bartolomeo Della Corte, Daniele Nardi, Giorgio Grisetti, Alberto Pretto
分類: cs.RO
原文アブストラクト
In this paper, we present a novel solution for real-time, Non-Linear Model Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The proposed controller formulates the Optimal Control Problem (OCP) in terms of flat outputs over an adaptive lattice. In common approximated OCP solutions, the number of discretization points composing the lattice represents a critical upper bound for real-time applications. The proposed NMPC-based technique refines the initially uniform time horizon by adding time steps with a sampling criterion that aims to reduce the discretization error. This enables a higher accuracy in the initial part of the receding horizon, which is more relevant to NMPC, while keeping bounded the number of discretization points. By combining this feature with an efficient Least Square formulation, our solver is also extremely time-efficient, generating trajectories of multiple seconds within only a few milliseconds. The performance of the proposed approach has been validated in a high fidelity simulation environment, by using an UAV platform. We also released our implementation as open source C++ code.
