Kernel-based identification using Lebesgue-sampled data

作     者：González, Rodrigo A. Tiels, Koen Oomen, Tom 

作者机构：Control Systems Technology Section Department of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands Delft Center for Systems and Control Delft University of Technology Delft Netherlands 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Impulse response 

摘      要：Sampling in control applications is increasingly done non-equidistantly in time. This includes applications in motion control, networked control, resource-aware control, and event-based control. Some of these applications, like the ones where displacement is tracked using incremental encoders, are driven by signals that are only measured when their values cross fixed thresholds in the amplitude domain. This paper introduces a non-parametric estimator of the impulse response and transfer function of continuous-time systems based on such amplitude-equidistant sampling strategy, known as Lebesgue sampling. To this end, kernel methods are developed to formulate an algorithm that adequately takes into account the bounded output uncertainty between the event timestamps, which ultimately leads to more accurate models and more efficient output sampling compared to the equidistantly-sampled kernel-based approach. The efficacy of our proposed method is demonstrated through a mass-spring damper example with encoder measurements and extensive Monte Carlo simulation studies on system benchmarks. © 2023, CC BY-NC-ND.