Data-Driven Iterative optimization of TDOF Controller with Rational Model

Taejune Kong, Hanul Jung, Sehoon Oh
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Abstract

For precise control, it can be achieved through a feedforward controller with a feedback control scheme. The feedforward controller can be automatically tuned with the datadriven methods through experimental results. However, these data-driven methods are interrupted when the input disturbance is applied. This problem can be solved using Disturbance Observer (DOB) in the control loop. In addition, a data-driven method derives accurate control performance by simultaneously tuning the feedforward controller and DOB model. Furthermore, a tuning algorithm with a rational model is proposed for the corresponding modeling to a flexible system. The simulation results verify that the proposed method accurately tunes the controller for an unknown plant under an input disturbance.
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基于Rational模型的TDOF控制器数据驱动迭代优化
为了精确控制,可以通过前馈控制器和反馈控制方案来实现。实验结果表明,采用数据驱动的方法可以实现前馈控制器的自动调谐。然而,当输入干扰存在时,这些数据驱动的方法被中断。在控制回路中引入干扰观测器(DOB)可以解决这一问题。此外,数据驱动方法通过同时调整前馈控制器和DOB模型来获得精确的控制性能。在此基础上,提出了一种具有合理模型的调优算法,对柔性系统进行相应的建模。仿真结果验证了该方法在输入扰动下对未知对象的控制器进行了准确的整定。
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