Complexity and Experiment Design in Identification for Control

系统辨识和基于模型的控制设计这两个学科之间的关系和相互作用是提出的研究的主要环境。没有答案的问题是，什么是控制设计的好模型？，将通过考虑迭代识别和控制方法中的一系列设计选择，从建设性的角度进行检查，该方法处理最近开发的基于闭环数据连续生成控制器和模型的技术。在这种迭代的背景下，每个设计变量的选择都是由之前的模型和控制器加上实验数据告知（或偏见）的。我们研究的设计参数与框架中的建模有关，其中建模不足和近似是常态，而不是寻求精确描述的不切实际的目标。将研究理论和实践两个领域，因为在这一领域，每一个领域都是重要的指导和辅助，在这一领域，成功的应用似乎领先于现有的理论支持。该研究具有很高的科学价值和实用价值，因为它涉及理解目的建模的本质和应用工业控制问题的基本问题。这两个领域的结果都是可以预期的。高性能控制系统被认为是许多工业应用的主要使能技术。由于提供动态模型是大多数高性能控制设计的起点，因此了解从闭环操作数据中导出该模型的方法对于实现这一潜力至关重要。本文讨论的研究领域是核心问题，其解决将为产生有用的实际技术提供理论理解和指导。

Relationships and interactions between the two disciplines of system identification and model-based control design are the prime milieu of the proposed research. The unanswered question, What is a good model for control design?, will be examined from a constructive viewpoint by considering a range of design choices within the methods of iterative identification and control, which deals with the recently developed techniques for successively generating controllers and models based on closed-loop data. In this context of iteration, each choice of design variable is informed (or prejudiced) by the previous model and controller plus experimental data. The design parameters which we study are connected with modeling in a framework where undermodeling and approximation are the norm, as opposed to the unrealistic aim of seeking exact descriptions. Both theoretical and practical areas will be studied, since each is an important guide and adjunct to the other in this field, where successful applications appear to be running ahead of the prevailing available theoretical support. The research has high scientific and practical merit, as it deals with fundamental problems of understanding the nature of modeling for a purpose and with issues of applied industrial control. Outcomes should be expected in both arenas. High performance control systems are regarded as a major enabling technology for many industrial applications. Since the provision of a dynamic model is the starting point for most high-performance control design, an understanding of the methodology for deriving this model from closed-loop operating data is critical to realizing this potential. The research areas addressed here are core issues whose resolution will provide the theoretical comprehension and guidance to yield useful practical techniques.