Multiobjective Robust Control of Linear Parameter Varying Systems and Applications

线性时不变系统的鲁棒控制已经得到了广泛的发展。在过去的二十年中，产生了强大的形式化理论，如h∞，mu合成/分析，以及最近的L-1最优控制，这些理论已成功地应用于具有挑战性的实际问题。相比之下，处理线性参数变化植物的实用工具刚刚出现，而且还远未完成。其中，有待解决的问题包括多种性能规范的非保守处理，设计方法和最终控制器的复杂性，以及与这些设计技术兼容的识别方法的发展。提出的研究旨在通过将LPV框架整合到精确处理广泛的性能规格和模型不确定性的能力中来消除这些限制。具体来说，拟议研究的目标是：开发用于LPV系统的鲁棒识别和模型验证的工具。鲁棒多目标LPV系统综合分析框架的发展。该框架将：(a)以非保守的方式处理多个性能规格；(b)查明系统性能的内在限制以及限制因素；(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机器人头和专用图像处理硬件进行实验验证。所提出的研究将结合功能分析和生存力理论的元素，成功地被PI用于解决LTI植物的多目标鲁棒识别和控制问题。初步结果表明，这种结合有可能在一个统一的框架内解决问题的识别和综合方面。这项研究工作将为LPV系统提供一个扩展的鲁棒控制框架，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，除了推动控制理论的最新发展，我们期望通过消除当前可用的LPV工具的一些限制，它将促进相关领域的进展。一个例子是计算机视觉，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公路系统到远程手术，具有广泛影响社会的潜力。＊＊＊

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. ***