ASAP-MPC: 非線形モデル予測制御によるオンライン軌道計画のための非同期更新スキーム
ASAP-MPC: An Asynchronous Update Scheme for Online Motion Planning with Nonlinear Model Predictive Control
NMPCの計算時間が変動・長大化しても非同期更新で完全収束とリアルタイム実行を両立し、線形状態フィードバックで軌道追従を安定化する手法を提案。ドローンやトラック・トレーラの実験で有効性を示した。
著者: Dries Dirckx, Mathias Bos, Bastiaan Vandewal, Lander Vanroye, Wilm Decré, Jan Swevers
分類: cs.RO, math.OC
原文アブストラクト
This paper presents a Nonlinear Model Predictive Control (NMPC) scheme targeted at motion planning for mechatronic motion systems, such as drones and mobile platforms. NMPC-based motion planning typically requires low computation times to be able to provide control inputs at the required rate for system stability, disturbance rejection, and overall performance. Although there exist various ways in literature to reduce the solution times in NMPC, such times may not be low enough to allow real-time implementations. This paper presents ASAP-MPC, an approach to handle varying, sometimes restrictively large, solution times with an asynchronous update scheme, always allowing for full convergence and real-time execution. The NMPC algorithm is combined with a linear state feedback controller tracking the optimised trajectories for improved robustness against possible disturbances and plant-model mismatch. ASAP-MPC seamlessly merges trajectories, resulting from subsequent NMPC solutions, providing a smooth and continuous overall trajectory for the motion system. This frameworks applicability to embedded applications is shown on two different experiment setups where a state-of-the-art method fails: a quadcopter flying through a cluttered environment in hardware-in-the-loop simulation and a scale model truck-trailer manoeuvring in a structured lab environment.
