Control and Sensing under Limited Information

有限信息下的控制与感知

• 批准号: 0701676
• 负责人: Daniel Liberzon
• 金额: $ 27万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-06-01 至 2011-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0701676&HistoricalAwards=false
• 关键词: Control; Sensing; under; Limited; Information

One crucial component that has been missing in automatic control so far is a good understanding of the coupling between control objectives and information constraints. If we view an overall closed-loop control system as a network of information-processing modules, the rate of information transfer between different components of the system in reality is never unlimited, due to constraints on the sensor capability, computational resources, and communication capacity. The increasing interest in high-performance networked embedded systems and autonomous robots puts in great demand a new control theory that can take information constraints into account. The primary goal of the proposed project is the development of a comprehensive performance-based theory of control with limited information. The main contribution of the project will be to address realistic nonlinear models for the control plants and sensors (e.g., those motivated by vision-based control) and to consider control and observer designs that provide guaranteed performance and robustness.Intellectual Merits:The new control theory will reveal fundamental capabilities and limitations of control-theoretic techniques in the constrained information context, thus bringing into better focus the role of systems and control theory in solving today's real-world problems. More specifically, we will verify the new theoretical results in the study of vision-based control of autonomous robots. We believe that the legacy of the proposed project will be a successful marriage of the mathematical control theory with an engineering problem of great practical importance.Broader Impacts: The proposed research will be naturally integrated with education through further improvement of recently developed courses on hybrid systems and control and computer vision, as well as through introduction of new courses on nonlinear control design and identification of hybrid models and systems. The proposers will ensure wide result dissemination and outreach through invited lectures, tutorial workshops, web-based materials, and involvement of students with diverse geographic and ethnic backgrounds in accordance with the policies of the University of Illinois.

到目前为止，自动控制中缺少的一个重要组成部分是对控制目标和信息约束之间的耦合的良好理解。如果我们将整个闭环控制系统看作一个信息处理模块的网络，由于传感器能力、计算资源和通信能力的限制，现实中系统不同组件之间的信息传输速度从来都不是无限的。随着人们对高性能网络嵌入式系统和自主机器人的兴趣与日俱增，迫切需要一种能够考虑信息约束的新型控制理论。拟议项目的主要目标是在信息有限的情况下发展一种全面的基于业绩的控制理论。该项目的主要贡献将是解决控制对象和传感器的现实非线性模型(例如，那些由视觉控制驱动的模型)，并考虑提供有保证的性能和健壮性的控制和观测器设计。智力优势：新的控制理论将揭示控制理论技术在受限信息环境中的基本能力和局限性，从而更好地关注系统和控制理论在解决当今现实世界问题中的作用。更具体地说，我们将在基于视觉的自主机器人控制研究中验证新的理论结果。我们相信，拟议项目的遗产将是数学控制理论与具有重大实际意义的工程问题的成功结合。广泛影响：拟议的研究将通过进一步改进最近开发的关于混合系统、控制和计算机视觉的课程，以及通过引入关于非线性控制设计和混合模型和系统的辨识的新课程，自然地与教育相结合。提倡者将根据伊利诺伊大学的政策，通过特邀讲座、辅导讲习班、基于网络的材料以及不同地理和种族背景的学生的参与，确保广泛的成果传播和推广。