Details

Autor(en) / Beteiligte

• Jiang, Wei
• Zhang, Xing long
• Zuo, Zhen
• Shi, Meiping
• Su, Shaojing

Titel

Data-driven Kalman Filter with Kernel-based Koopman Operators for Nonlinear Robot Systems

Ist Teil von

• 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022, p.12872-12878

Ort / Verlag

IEEE

Erscheinungsjahr

2022

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• Designing the Kalman filter for nonlinear robot systems with theoretical guarantees is challenging, especially when the dynamics model is unavailable. This paper proposes a data-driven Kalman filter algorithm using kernel-based Koop-man operators for unknown nonlinear robot systems. First, the Koopman operator using sparse kernel-based extended dynamic decomposition (EDMD) is presented to learn the unknown dynamics with input-output datasets. Unlike classic EDMD, which requires manual selection of kernel functions, our approach automatically constructs kernel functions using an approximate linear dependency analysis method. The resulting Koopman model is a linear dynamic evolution in the kernel space, enabling us to address the nonlinear filtering problem using the standard linear Kalman filter design process. Despite this, our approach generates a nonlinear filtering law thanks to the adopted nonlinear kernel functions. Finally, the effectiveness of the proposed approach is validated by simulated experiments.