Design of Control Systems based on Data-Matching Using Input-Output Measurements

我们考虑了基于输入/输出测量为未知对象设计闭环系统的几个重要问题。主要研究结果如下：1.研究结果。我们提出了一种直接的方法，通过从输入/输出测量和期望响应中进行控制器辨识来获得期望控制器的参数，而不是通过对被控对象的模型辨识。该方法是对已有的VRFT和FRIT方法的扩展。该方法的一个显著特点是能够通过辨识误差来检测意外的闭环系统不稳定。我们讨论了所得参数的最优性问题，并给出了理论结果，即应用预滤波使我们能够获得实现最优跟踪性能的实际最优参数。在此理论结果的基础上，我们还将FRIT扩展到了更实用的方法，即优化可以在线性计算中进行，这意味着本文得到的结果可以用更简单的计算来检验。提出了基于控制器整定的系统辨识的“控制器辨识”的新概念。也就是说，通过调整包含对象结构的控制器，我们获得了反映数据信息和给定指标的对象的最优参数。我们已经解决了现有的正则控制器和拟议的FRIT之间的联系。在我们的框架中，我们重新制定了规范控制器。因此，我们得到了精炼的熔块，从理论上看，在熔块中进行的调整方法产生了符合数据和给定规格的参数。我们对所提出的方法进行了扩展，得到了在对象动态变化情况下一致获得控制性能的单一控制器。

We consider several important issues concerned with designing a closed loop system for an unknown plant based on input/output measurements. The main results are as follows.1. We have proposed a direct method to obtain parameters of the desired controller via controller identification from the input/output measurements and the desired response instead of model identification of the plant. The proposed method is an extension of the existing method, i.e., VRFT and FRIT. A salient feature of the proposed method is to be able to detect unexpected closed loop instability by using identification error.2. We addressed the issues on the optimality of the parameter obtained FRIT, and have provided the theoretical result that applying a prefilter enables us to obtain the actual optimal parameter for the achievement of the optimal tracking performance. Based on this theoretical result, we have also expanded FRIT into more practical method in the sense that the optimization can be performed in the linear computation, which implies that the result obtained here can be examined by more simple computation.3. We have proposed a new concept "identification of a plant via controller identification" of system identification based on controller tuning. That is, by tuning a controller including the structure of a plant, we have an optimal parameter of the plant reflecting the information of the data and the given specification.4. We have addressed the connection between an existing canonical controller and proposed FRIT. We have reformulated canonical controllers in our framework. As a result, we have refined FRIT in the sense that the tuning method done in FRIT yields the parameter fitting the data and the given specification from the theoretical points of view.5. We have extended the proposed method to obtain a single controller which uniformly achieves control performance under plant dynamics variation.