不確かな外乱下の非線形システムに対する学習ベース予測経路追従制御
Learning-based Predictive Path Following Control for Nonlinear Systems Under Uncertain Disturbances
ガウス過程で外乱をオンライン学習する低レベル制御器と、モデル予測経路追従制御器を組み合わせ、ドローンの風外乱下での経路追従精度を高める手法を提案した。
著者: Rui Yang, Lei Zheng, Jiesen Pan, Hui Cheng
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Accurate path following is challenging for autonomous robots operating in uncertain environments. Adaptive and predictive control strategies are crucial for a nonlinear robotic system to achieve high-performance path following control. In this paper, we propose a novel learning-based predictive control scheme that couples a high-level model predictive path following controller (MPFC) with a low-level learning-based feedback linearization controller (LB-FBLC) for nonlinear systems under uncertain disturbances. The low-level LB-FBLC utilizes Gaussian Processes to learn the uncertain environmental disturbances online and tracks the reference state accurately with a probabilistic stability guarantee. Meanwhile, the high-level MPFC exploits the linearized system model augmented with a virtual linear path dynamics model to optimize the evolution of path reference targets, and provides the reference states and controls for the low-level LB-FBLC. Simulation results illustrate the effectiveness of the proposed control strategy on a quadrotor path following task under unknown wind disturbances.
