Practical Methods for the Identification of Nonlinear Systems

非线性系统辨识的实用方法

• 批准号: RGPIN-2020-04590
• 负责人: Westwick, David
• 金额: $ 2.04万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=717123
• 关键词: Practical; Methods; Identification; Nonlinear; Systems

System identification refers to a class of data-driven techniques that construct mathematical models of dynamic systems from measurements of their input(s) and output(s). Closely related to machine learning and artificial intelligence, system identification is the enabling technology behind the design of high-performance control systems, automatic fault detection methods and the quantitative study of dynamic systems. Initial work focused on the identification of linear, time-invariant systems, and has produced a mature technology documented in numerous textbooks and supported by several commercially available tools. The identification of nonlinear and/or time-varying systems, on the other hand, is still an active research field whose application is still largely restricted to specialists in the field. The long-term goal of the proposed research program is to close the gap between the theoretical development and practical application of nonlinear system identification methods. In particular, we will create methods that can be used by nonspecialists that can identify a broad class of systems that are of practical interest using data gathered under typical experimental conditions. In moving toward this objective, my team will pursue two complimentary lines of research: The first research theme will develop methods for the identification of nonlinear systems that are operating inside of feedback control loops. In a control engineering context, closed-loop operation is often required either for economic or safety reasons. The presence of a feedback loop adds complexity to the identification, as errors in the output measurement eventually appear in the system's input, due to the feedback loop. Thus, the assumption that the disturbances are independent of the system input breaks down in this case. While solutions for closed-loop identification of linear systems have been extensively developed, their extension to nonlinear systems remains an open problem. The second major theme involves developing robust, “turn-key” methods for the identification of nonlinear state-space (NLSS) systems. This class of systems has been shown to be much broader that the class of systems that can be represented by a Volterra series, in that it includes systems, when linearized, whose poles move with changes in the system's operating point. NLSS models, which can also represent any Volterra system, can also represent systems with moving poles. My team will develop methods for the identification of these systems, that can be used with a minimum of user input. Finally, the two themes will be merged, and the methods for NLSS identification will be extended to enable the use of data gathered in closed-loop. The ability to identify nonlinear system dynamics in closed loop will have a significant impact in a variety control applications by eliminating the need for dedicated model calibration experiments thus reducing the cost associated with control system design.

系统识别是指一类数据驱动的技术，该技术从其输入和输出的测量值中构建动态系统的数学模型。系统识别与机器学习和人工智能密切相关，是高性能控制系统，自动故障检测方法和动态系统的定量研究背后的促成技术。最初的工作重点是识别线性，时间不变的系统，并在许多教科书中记录了一项成熟的技术，并得到了几种可商购的工具的支持。另一方面，非线性和/或时间变化系统的识别仍然是一个活跃的研究领域，其应用仍在很大程度上仅限于该领域的专家。 拟议的研究计划的长期目标是弥合非线性系统识别方法的理论发展与实际应用之间的差距。特别是，我们将创建可以使用非专业人士使用的方法，这些方法可以识别出使用典型的实验条件下收集的数据具有实际意义的广泛系统。在朝着这一目标迈进时，我的团队将追求两条免费的研究线： 第一个研究主题将开发用于识别反馈控制循环内部运行的非线性系统的方法。在控制工程环境中，出于经济或安全原因，通常需要进行闭环操作。反馈回路的存在增加了标识的复杂性，因为由于反馈回路，输出测量中的错误最终会出现在系统输入中。这是在这种情况下，灾难与系统输入无关的假设。尽管已广泛开发了用于线性系统闭环识别的解决方案，但它们向非线性系统的扩展仍然是一个开放的问题。 第二个主要主题涉及开发可识别非线性状态空间（NLSS）系统的强大的“交钥匙”方法。这类系统已被证明更广泛，即可以用Volterra系列表示的系统类别，因为它包括系统，当线性化时，其极点会随着系统操作点的变化而移动。 NLSS模型也可以代表任何Volterra系统，也可以代表带有移动杆的系统。我的团队将开发识别这些系统的方法，这些方法可与最少的用户输入一起使用。最后，将合并两个主题，并将扩展NLSS识别方法以实现在闭环中收集的数据的使用。 通过消除对专用模型校准实验的需求，从而降低了与控制系统设计相关的成本，可以在封闭环中识别非线性系统动力学的能力对各种控制应用产生重大影响。