Linear and Nonlinear Indentification of Low Complexity Uncertain Models

9912533 Chen这一建议启动了一项诺贝尔研究计划，进入面向控制的辨识领域，研究用于辨识低复杂度不确定模型的线性和非线性方法。 其主要目标是开发新的，理论上合理的和实际有用的识别理论和技术，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的基本利益和问题的重要相关性，实践工程师，与特定的应用领域集中在主动噪声抑制和燃烧不稳定性问题的问题。 该研究计划涉及悬而未决的问题，并呼吁注意新出现的问题领域。 它特别认识到计算和模型的复杂性作为关键的障碍，和非线性识别和模型验证作为新的推力领域，这两个被认为是有潜力发展成为重要的研究领域，在系统和控制研究。 要完成的主要技术目标是：调查一个新的范例混合确定性/概率识别问题。 本课题主要研究在概率/随机噪声假设下确定性不确定模型的辨识与验证技术和算法，研究特殊类型非线性系统的非线性辨识与模型验证方法。 重点将放在非线性系统与典型的工程相关的无记忆非线性。 本课程的主要目标是辨识和验证具有极限环的非线性动力系统，并通过模拟和实验来测试和指导理论的发展。 提出了噪声控制和燃烧不稳定性问题作为应用和测试的基准，并针对这些问题制定了详细的研究计划，提出了具体、现实的解决方案，具有理论意义和实际可行性。 该计划旨在融合理论，仿真和实验，并得到经典插值理论，凸优化，随机过程，系统识别，鲁棒控制和非线性动力学中的成熟概念和工具的支持。 人们认为，该项目的成功完成将显着影响当代以及未来的理论和应用研究领域的系统识别和控制。 在短期内，它将显著地推进当前最先进的识别技术，在计算上有效的识别算法的发展。 从长远来看，它将提供洞察力和推力的关键问题和非线性动力系统的建模和识别中发现的问题。 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，因此有可能产生直接影响。该计划预计从2000年7月1日至2003年6月30日为期三年。 支持要求PI，为两个夏季全职，每年，并为一名研究生，在整个颁奖期间。 已完成的项目将产生新的、理论上重要的和实际可行的识别方法作为主要交付成果，包括：基于混合确定性/概率性设置的识别和模型验证的完全开发的范例和算法，非线性识别和模型验证技术，混合确定性/概率性范例和非线性模型验证方法的基准示例和问题，测试的噪声控制数据和燃烧模型。 ***

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. ***