Linear and Nonlinear Indentification of Low Complexity Uncertain Models

低复杂度不确定模型的线性和非线性识别

• 批准号: 9912533
• 负责人: Jie Chen
• 金额: $ 15万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-15 至 2004-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9912533&HistoricalAwards=false
• 关键词: Linear; Nonlinear; Indentification; Low; Complexity

9912533ChenThis proposal initiates a nobel research program into the area of control-oriented identication, investigating linear and nonlinear methods for identification of low-complexity uncertain models. The principal goal is to develop new, theoretically sound and practically useful identification theories and techniques for modeling physical systems in a way compatible to control design theory and practice. The program is motivated and guided by problems of fundamental interest and problems of significant relevance to practicing engineers, with specific application areas focused on active noise suppression and combustion instability problems. The research plan addresses outstanding issues and calls for attention to new, emerging problem areas. It recognizes specifically computational and model complexity as the key obstacle, and nonlinear identification and model validation as the new thrust area, both of which are believed to have the potential to develop into important research areas in systems and control research. The main technical objectives to be accomplished are:To investigate a new paradigm of mixed deterministic/probabilistic identification problems. The task attempts to provide techniques and algorithms for identification and validation of deterministic uncertain models under probabilistic/stochastic noise assumptions.To develop nonlinear identification and model validation methods for special classes of nonlinear systems. The main focus will be on nonlinear systems with typical memoryless nonlinearities of engineering relevance. A central thrust will be the identification and validation of nonlinear dynamical systems with limit cycles.To test and gu ide the theoretical development via simulation and experimental work. Noise control and combustion instability problems are identified as application and testing benchmarks.A detailed research plan has been formulated to address these issues, which consists of concrete, realistic solution strategies that are believed to be both theoretically signficant and practically feasible. The plan seeks to merge theory, simulation, and experimentation, and is supported by well-established concepts and tools found in classical interpolation theory, convex optimization, stochastic processes, system identification, robust control, and nonlinear dynamics. It is felt that the successful completion of this project would significantly impact the contemporary as well as future theoretical and applied research in the areas of system identification and control. In the short term, it would advance signficantly the current state-of-the-art identification techniques, in development of computationally efficient identification algorithms. In the long term, it would lend insight and thrust to key issues and problems found in modeling and identification of nonlinear dynamical systems. Additionally, the application studies link the program directly to, and hence have the the potential to be of direct impact, on a number of ongoing research projects currently under pursuit in industrial R&D laboratories.The program is projected to span a course of three years from July 1, 2000 to June 30, 2003. Support is requested for the PI, for two summer months full time, each year, and for one graduate student, throughout the entire award period. The completed project will yield as main deliverables new, theoretically significant and practically feasible identification approaches, consisting of:a fully developed paradigm and algorithms for identification and model validation based upon a mixed deterministic/probabilistic setting,nonlinear identification and model validation techniques,worked-out benchmark examples and problems for the mixed deterministic/probabilistic paradigm and the nonlinear model validation methds,Tested noise control data and combustion models. ***

9912533这项提议启动了面向控制的辨识领域的诺贝尔研究计划，研究用于识别低复杂性不确定模型的线性和非线性方法。主要目标是发展新的、理论上合理的、实用的辨识理论和技术，以符合控制设计理论和实践的方式对物理系统进行建模。该计划的动机和指导是基本感兴趣的问题和与执业工程师密切相关的问题，具体应用领域集中在有源噪声抑制和燃烧不稳定问题上。研究计划涉及悬而未决的问题，并呼吁关注新出现的问题领域。它特别指出计算和模型复杂性是关键障碍，而非线性辨识和模型验证是新的推动力领域，这两个领域都被认为有可能发展成为系统和控制研究的重要研究领域。要完成的主要技术目标是：研究一种混合确定性/概率识别问题的新范式。本课题旨在为概率/随机噪声假设下的确定性不确定模型的辨识和验证提供技术和算法，发展一类特殊类型的非线性系统的非线性辨识和模型验证方法。主要的焦点将放在具有典型的与工程相关的无记忆非线性的非线性系统上。一个中心推力将是具有极限环的非线性动力系统的辨识和验证。通过仿真和实验工作来检验和指导理论的发展。噪声控制和燃烧不稳定问题被确定为应用和测试基准。针对这些问题，已经制定了详细的研究计划，其中包括具体的、现实的解决策略，被认为具有理论意义和实际可行性。该计划寻求融合理论、模拟和实验，并得到经典内插理论、凸优化、随机过程、系统辨识、鲁棒控制和非线性动力学中成熟的概念和工具的支持。人们认为，该项目的成功完成将对当代以及未来在系统识别和控制领域的理论和应用研究产生重大影响。在短期内，它将在开发计算效率高的识别算法方面显著推进当前最先进的识别技术。从长远来看，它将对非线性动力系统建模和辨识中的关键问题和问题提供洞察力和推动力。此外，应用研究将该计划直接与工业研发实验室目前正在进行的一些研究项目联系起来，因此有可能对其产生直接影响。该计划预计将从2000年7月1日起持续三年，至2003年6月30日。在整个获奖期内，要求每年为PI提供两个夏季全职时间的支持，并为一名研究生提供支持。完成的项目将产生新的、理论上有意义的和实际可行的识别方法，包括：基于混合确定性/概率设置的完全开发的识别和模型验证的范例和算法、非线性识别和模型验证技术、针对混合确定性/概率范式和非线性模型验证方法制定的基准范例和问题、测试的噪声控制数据和燃烧模型。***