パラメータ不確かさを伴うハイブリッド線形システムに対するロバスト陽的モデル予測制御
Robust explicit model predictive control for hybrid linear systems with parameter uncertainties
ハイブリッド線形システムのパラメータ不確かさに対応するため、ゾノトープベースの陽的MPCを拡張し、凸ゾノトープ次元削減法と準時間不要な政策選択アルゴリズムを提案。実験で有効性を検証。
著者: Oleg Balakhnov, Sergei Savin, Alexandr Klimchik
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Explicit model-predictive control (MPC) is a widely used control design method that employs optimization tools to find control policies offline; commonly it is posed as a semi-definite program (SDP) or as a mixed-integer SDP in the case of hybrid systems. However, mixed-integer SDPs are computationally expensive, motivating alternative formulations, such as zonotope-based MPC (zonotopes are a special type of symmetric polytopes). In this paper, we propose a robust explicit MPC method applicable to hybrid systems. More precisely, we extend existing zonotope-based MPC methods to account for multiplicative parametric uncertainty. Additionally, we propose a convex zonotope order reduction method that takes advantage of the iterative structure of the zonotope propagation problem to promote diagonal blocks in the zonotope generators and lower the number of decision variables. Finally, we developed a quasi-time-free policy choice algorithm, allowing the system to start from any point on the trajectory and avoid chattering associated with discrete switching of linear control policies based on the current state's membership in state-space regions. Last but not least, we verify the validity of the proposed methods on two experimental setups, varying physical parameters between experiments.
