Robust Control of Constrained Linear Parameter Varying Systems and Applications

约束线性参数变化系统的鲁棒控制及其应用

• 批准号: 0115946
• 负责人: Mario Sznaier
• 金额: $ 10万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0115946&HistoricalAwards=false
• 关键词: Robust; Control; Constrained; Linear; Parameter

0115946SznaierRobust control of linear time invariant systems has undergone extensive developments in the past two decades, leading to powerful formalisms such as H8, u-synthesis/analysis and, more recently, l1optimal control, that have been successfully applied to challenging practical problems. In contrast, tools for handling linear parameter varying plants have just emerged and are still far from complete. An open issue, central to many practical problems, is the non-conservative handling of constraints, both on the outputs and in the control action. The proposed research is aimed at addressing this issue by incorporating into the LPV frame-work the capability to deal exactly with a broad class of performance specifications and model uncertainty. Specifically, the objectives of the proposed research are:O Development of an analytical framework for synthesizing robust LPV systems subject to hard constraints. This framework should exhibit the following properties:(a) Handle control and output constraints in a non-conservative fashion;(b) Identify the intrinsic limits of performance of the system as well as the limiting factors; and(c) Result in computationally tractable procedures leading to practically implementable controllers.O Application of the resulting theory to several problems spanning a broad spectrum of applications such as active vision and oil prospection.The proposed research will combine elements from functional analysis, viability theory and dynamical systems theory, following an approach successfully used by the co-PIs to handle constraints in the case of LTI systems. Preliminary results indicate that this approach leads to a framework with the desired features.The PI expects that this research effort will result in an expanded robust control framework for LPV systems, capable of addressing realistic problems necessitating neither potentially conservative approximations nor multiple trial and error type iterations. Moreover, in addition to advancing the state of the art in control theory, he expects that by removing some of the limitations of currently available LPV tools, it will foster progress in related areas. An example is computer vision, an area where recent technological advances have rendered a number of practical applications feasible, provided that certain related control issues can be resolved. These applications range from intelligent highway systems to remote surgery and have the potential to broadly impact society.The proposed research will also have a direct bearing upon the quality of graduate and undergraduate education both at Penn State University (PSU) and the Universidad Autonoma Metropolitana (UAM). In addition to direct student involvement and incorporation of the results into the curriculum, it will allow students from the UAM to use state-of-the-art computer vision equipment available at PSU, while Penn State students will benefit from having access to proprietary data and experiments from the Mexican Petroleum Institute.

0115946 Sznaier线性时不变系统的鲁棒控制在过去的二十年里经历了广泛的发展，导致了强大的形式主义，如H8，u-综合/分析，最近，l1最优控制，已成功地应用于具有挑战性的实际问题。相比之下，处理线性参数变化的植物的工具刚刚出现，仍然远远没有完成。一个开放的问题，许多实际问题的核心，是非保守的处理约束，无论是在输出和控制行动。拟议的研究旨在解决这个问题，纳入LPV框架的能力，以准确地处理广泛的性能规格和模型的不确定性。 具体而言，所提出的研究的目标是：O发展的分析框架，用于合成鲁棒LPV系统受到硬约束。 这种框架应具有以下特性：（a）以非保守的方式处理控制和输出约束;（B）确定系统性能的内在限制以及限制因素;以及（c）结果在计算上易于处理的程序，导致实际可实施的控制器。应用所产生的理论，以跨越广泛的应用，如主动视觉和石油勘探的几个问题。拟议的研究将联合收割机元素从功能分析，可行性理论和动力系统理论，以下的方法成功地使用的共同PI处理约束的情况下，LTI系统。初步结果表明，这种方法导致一个框架所需的feature.The PI预计，这项研究工作将导致在LPV系统的扩展鲁棒控制框架，能够解决现实问题，既不需要潜在的保守近似，也不需要多次试验和错误类型的迭代。此外，除了推进控制理论的最新发展外，他预计通过消除目前可用的LPV工具的一些限制，它将促进相关领域的进展。计算机视觉就是一个例子，在这个领域，最近的技术进步已经使许多实际应用变得可行，前提是某些相关的控制问题可以得到解决。这些应用范围从智能高速公路系统到远程手术，并有可能广泛影响社会。拟议的研究也将直接影响宾夕法尼亚州立大学（PSU）和大都会自治大学（UAM）的研究生和本科生教育质量。除了学生直接参与并将结果纳入课程之外，它还将允许UAM的学生使用PSU提供的最先进的计算机视觉设备，而宾夕法尼亚州立大学的学生将受益于获得专有数据和墨西哥石油研究所的实验。