识别Lebesgue采样连续时间脉冲响应模型：一种基于核的方法
Identifying Lebesgue-sampled Continuous-time Impulse Response Models: A Kernel-based Approach
AI总结：
针对采用Lebesgue采样策略的连续时间系统，本文提出一种基于核方法的非参数脉冲响应估计器，通过充分考虑输出样本间行为提升模型准确性与采样效率。
摘要：
控制应用在时间上越来越多地采用非等距采样，包括运动控制、网络化控制、资源感知控制和事件触发控制。其中一些应用使用了在幅度域进行等距采样的测量设备。本文旨在基于这种被称为Lebesgue采样的策略，开发一种连续时间系统脉冲响应的非参数估计器。为此，我们开发了核方法以构建一种算法，该算法充分考虑了输出的样本间行为，与标准方法相比，最终能够得到更准确的模型和更高效的输出采样。通过质量-弹簧阻尼器案例研究验证了该方法的有效性。
英文摘要：
Control applications are increasingly sampled non-equidistantly in time, including in motion control, networked control, resource-aware control, and event-triggered control. Some of these applications use measurement devices that sample equidistantly in the amplitude domain. The aim of this paper is to develop a non-parametric estimator of the impulse response of continuous-time systems based on such sampling strategy, known as Lebesgue-sampling. To this end, kernel methods are developed to formulate an algorithm that adequately takes into account the output intersample behavior, which ultimately leads to more accurate models and more efficient output sampling compared to the standard approach. The efficacy of this method is demonstrated through a mass-spring damper case study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
