Multiobjective Robust Control of Linear Parameter Varying Systems and Applications

线性定常系统的鲁棒控制已经得到了广泛的发展。 在过去的二十年里，导致强大的形式主义，如H-无穷大，μ-综合/分析，最近，L-1最优控制，已成功地应用于具有挑战性的实际问题。 与此相反，处理线性参数变化的植物的实用工具刚刚出现，仍然远远没有完成。 其中，尚未解决的问题包括非保守处理的多个性能指标，复杂的设计方法和由此产生的控制器，并与这些设计技术兼容的识别方法的发展。 拟议的研究旨在消除这些限制，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，拟议的研究目标是：开发工具的鲁棒识别和模型验证的LPV系统。开发一个分析框架，综合强大的多目标LPV系统。这一框架将：（a）以非保守的方式处理多种性能规格;（B）查明系统性能的内在限制以及限制因素;以及（c）解决控制器复杂性问题。将所得理论应用于主动视觉系统鲁棒控制器的合成，并使用Bisight机器人头部和专用图像处理硬件进行实验验证。拟议的研究将联合收割机元素从功能分析和生存理论，成功地用于PI解决多目标鲁棒识别和控制问题的LTI植物。 初步结果表明，这种组合有可能解决，在一个统一的框架，无论是识别和合成方面的问题，这项研究工作将导致在一个扩展的鲁棒控制框架LPV系统，能够解决现实问题，既不需要潜在的保守的近似，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，我们还希望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。 ***

9907051SznaierRobust control of linear time invariant systems has undergone extensive developments. In the past two decades, leading to powerful formalisms such as H-infinity, mu-synthesis/analysis and, more recently, L-1 optimal control, that have been successfully applied to challenging practical problems. In contrast, practical tools for handling linear parameter varying plants have just emerged and are still far from complete. Among others, issues yet to be resolved include non-conservative handling of multiple performance specifications, complexity of both the design method and the resulting controllers, and the development of identification methods compatible with these design techniques. The proposed research is aimed at removing these limitations by incorporating into the LPV framework the capability to deal exactly with a broad class of performance specifications and model uncertainty. Specifically, the objectives of the proposed research are:Development of tools for robust identification and model validation of LPV systems.Development of an analytical framework for synthesizing robust multiobjective LPV systems. This framework will:(a) Handle multiple performance specifications in a non-conservative fashion;(b) Identify the intrinsic limits of performance of the system as well as the limiting factors; and(c) Address the issue of controller complexity.Application of the resulting theory to the synthesis of robust controllers for active vision systems and experimental validation using a Bisight robotic head and dedicated image processing hardware.The proposed research will combine elements from functional analysis and viability theory, successfully used by the PI to solve multiobjective robust identification and control problems for LTI plants. Preliminary results show that this combination has the potential to address, in a unified framework, both the identification and synthesis aspects of the problem.This research effort will result in an expanded robust control framework for LPV systems, capable of addressing realistic problems necessitating neither potentially conservative approxi-mations nor multiple trial and error type iterations. Moreover, in addition to advancing the state of the art in control theory, we expect 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. ***