2023/05/07 by Yuhan Li, Li, Yuhan, Xiaoqiang Ji +1
Engineering · Physics and Astronomy · #FOS: Electrical engineering #Hydraulic and Pneumatic Systems #Model Reduction and Neural Networks #Structural Health Monitoring Techniques #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2305.04158
openalex publication_date 2023/05/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper, a novel Koopman-type inverse operator for linear time-invariant non-minimum phase systems with stochastic disturbances is proposed. This operator employs functions of the desired output to directly calculate the input. Furthermore, it can be applied as a data-driven approach for systems with unknown parameters yet a known relative degree, which is a departure from the majority of existing data-driven methods that are only applicable to minimum phase systems. Based on this foundation, we use the Monte Carlo approach to develop an improved Koopman-type method for addressing the issue of inaccurate parameter estimation in data-driven systems with large disturbances. The simulation results justify the tracking accuracy of Koopman-type operator.