入力制約下の未知システムに対する全クラス信号時相論理タスクの時空間チューブ学習制御
Learning Spatiotemporal Tubes for Full Class of Signal Temporal Logic Tasks for Control of Unknown Systems under Input Constraints
未知の非線形オイラー・ラグランジュ系に対し、入力制約を考慮しつつ信号時相論理仕様を満たす時空間チューブ制御を提案。物理情報ニューラルネットでチューブを学習し、閉形式制御則で軌道を管内に維持する。
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著者: Ahan Basu, Ratnangshu Das, Soumyodipta Nath, Siyuan Liu, Pushpak Jagtap
分類: cs.RO
原文アブストラクト
This paper presents a Spatiotemporal Tube (STT)-based control framework for general unknown nonlinear Euler-Lagrange (EL) systems subject to input constraints, with the objective of satisfying Signal Temporal Logic (STL) specifications, where confinement of the system trajectory within the STT guarantees the satisfaction of the corresponding STL task. For both single and multi-agent scenarios, the STT corresponding to each agent is modeled as a time-varying ball, whose center and radius are jointly parameterized using a physics-informed neural network (PINN). The robustness metric associated with the STL specification corresponding to the agents is incorporated into the training process as a loss function, enabling the learned tube to encode task-level temporal requirements. For a multi-agent scenario, we introduce an additional robustness metric corresponding to the global task, which, when satisfied, ensures the tubes do not collide with each other. To ensure that the system trajectory remains within the learned STT and thereby satisfies the local and global STL specifications, we propose a control strategy that explicitly accounts for input constraints. In particular, a closed-form control law is developed to keep the trajectory inside the tube while regulating the motion of the tube by enforcing bounds on its evolution depending on the input constraints of the system. The proposed approach has been validated over several case studies.
