Closed-Loop Output Error Approaches for Drone's Physics Informed Trajectory Inference

• 成果类型: Article
• 署名作者: Perrusquia, Adolfo; Guo, Weisi
• 署名单位: Cranfield University
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3247461
• 发表日期: 2023
• 页码: 7824-7831
• 关键词: Admittance model; drones; full/partial states measurements; physics informed model; trajectory inference

摘要

The design of adequate countermeasures against drone's threats needs accurate trajectory estimation to avoid economic damage to the aerospace industry and national infrastructure. As trajectory estimation algorithms need highly accurate physics informed models or offline learning algorithms, radical innovation in online trajectory inference is required. In this article, a novel drone's physics informed trajectory inference algorithm is proposed. The algorithm constructs a physic informed model and infers the drone's trajectories simultaneously using a closed-loop output error architecture. Two different approaches are proposed based on a physics structure and an admittance filtering model which considers: 1) full states measurements and 2) partial states measurements. Stability and convergence of the proposed schemes are assessed using Lyapunov stability theory. Simulations studies are carried out to demonstrate the scope and high inference capabilities of the proposed approach.