论文标题
使用固定极观察者模型对连续时间系统进行直接闭环识别
Direct closed-loop identification of continuous-time systems using fixed-pole observer model
论文作者
论文摘要
本文提供了一种仅从输入输出数据中获取闭环系统中目标系统的连续时间模型的方法，如果没有对控制器或激发信号的了解，并且I/O数据可能会遭受未知的偏移量。所提出的方法基于固定杆观察者模型，该模型是一个合理的连续时间版本，与离散时间的创新模型相对应，并允许识别不稳定的目标系统。此外，可以通过固定观察者杆来将提出的方法归因于凸优化问题。该方法在稳定的输出误差方法的框架内，并具有可用性优势，例如具有复杂动力学的噪声和对广泛模型的适用性的稳健性。该方法的有效性通过数值示例说明。
This paper provides a method for obtaining a continuous-time model of a target system in closed-loop from input-output data alone, in the case where no knowledge of the controllers nor excitation signals is available and I/O data may suffer from unknown offsets. The proposed method is based on a fixed-pole observer model, which is a reasonable continuous-time version corresponding to the innovation model in discrete-time and allows the identification of unstable target systems. Furthermore, it is shown that the proposed method can be attributed to a convex optimization problem by fixing the observer poles. The method is within the framework of the stabilized output error method and shares usability advantages such as robustness to noise with complex dynamics and applicability to a wide class of models. The effectiveness of the method is illustrated through numerical examples.
