A Diagnostic Modeling Methodology for Dual Retrospective Cost Adaptive Control of Combustion

双回溯成本自适应燃烧控制的诊断建模方法

• 批准号: 1634709
• 负责人: Dennis Bernstein
• 金额: $ 125万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1634709&HistoricalAwards=false
• 关键词: Diagnostic; Modeling; Methodology; Dual; Retrospective

Control technology makes it possible to use robotics for manufacturing and autopilots for autonomous vehicles. Control technology enhances productivity, efficiency, and safety. Applications that are especially challenging require computer algorithms that can adapt to unpredictable changes in the system and its environment. This project will use adaptive control to improve the performance of engines that burn fuel in combustion processes. These engines are used worldwide to generate energy for the electrical grid. The challenging problem is to burn the fuel more efficiently and reduce pollution despite changes in the demand for electricity. The technology developed under this project will enhance the operation and reliability of the electrical grid while reducing the emission of greenhouse gases and soot particles. The project will involve students from multiple engineering disciplines and will enhance the diversity of future professionals working in this area of technology. This project will advance knowledge and understanding in the theory and practice of feedback control by developing diagnostic modeling techniques for retrospective cost adaptive control (RCAC). The modeling information required by RCAC concerns the presence of specific features (such as right-half-plane zeros and nonlinearities) as well as the accuracy with which those features must be known (locations of the right-half-plane zeros and details of the nonlinearities). As an extension of RCAC, adaptation and closed-loop identification are performed concurrently as dual RCAC (DRCAC). This technique depends on efficient algorithms for biquadratic optimization. If identification with DRCAC using feedback sensors fails to reveal the essential modeling details, then non-feedback sensors with more in-depth diagnostic capability will be used to probe the system to obtain data for calibrating reduced-fidelity models. These models will be used by DRCAC for online analysis and closed-loop simulation, and, if necessary, the feedback sensing and actuation strategy will be modified. The intellectual objective of this project is a deeper understanding of dual control and the development of the diagnostic methodology in order to facilitate adaptive control of complex systems in theory and practice.

控制技术使机器人技术用于制造和自动驾驶汽车成为可能。控制技术提高了生产力、效率和安全性。特别具有挑战性的应用程序需要能够适应系统及其环境中不可预测变化的计算机算法。该项目将使用自适应控制来提高在燃烧过程中燃烧燃料的发动机的性能。 这些发动机在世界范围内用于为电网发电。 具有挑战性的问题是，尽管电力需求发生了变化，但如何更有效地燃烧燃料并减少污染。 该项目开发的技术将提高电网的运行和可靠性，同时减少温室气体和烟尘颗粒的排放。 该项目将涉及来自多个工程学科的学生，并将提高未来在这一技术领域工作的专业人员的多样性。本项目将通过开发追溯成本自适应控制（RCAC）的诊断建模技术，提高对反馈控制理论和实践的认识和理解。RCAC所需的建模信息涉及特定特征（如右半平面零点和非线性）的存在以及必须知道这些特征的准确性（右半平面零点的位置和非线性的细节）。 作为RCAC的扩展，自适应和闭环识别作为双RCAC（DRCAC）同时执行。这种技术依赖于双二次优化的有效算法。如果使用反馈传感器的DRCAC识别无法揭示基本的建模细节，则将使用具有更深入诊断能力的非反馈传感器来探测系统，以获得用于校准降保真模型的数据。DRCAC将使用这些模型进行在线分析和闭环仿真，必要时还将修改反馈传感和驱动策略。该项目的智力目标是更深入地理解双重控制和诊断方法的发展，以促进复杂系统的自适应控制的理论和实践。

项目成果

A Time-Delayed Lur’e Model with Biased Self-Excited Oscillations

具有偏置自激振荡的时滞 Lurâe 模型

• DOI: 10.23919/acc45564.2020.9147761
• 发表时间: 2020
• 期刊: Proc. American Control Conference
• 作者: Paredes, Juan;Ul Islam, Syed Aseem;Bernstein, Dennis S.
• 通讯作者: Bernstein, Dennis S.

Identification of Self-Excited Systems Using Discrete-Time, Time-Delayed Lur'e Models

使用离散时间、时滞 Lure 模型识别自激系统

• DOI: 10.23919/acc50511.2021.9482805
• 发表时间: 2020
• 期刊: 2021 American Control Conference (ACC)
• 作者: J. Paredes;D. Bernstein
• 通讯作者: D. Bernstein

Output-only identification of self-excited systems using discrete-time Lur'e models with application to a gas-turbine combustor

使用离散时间 Lure 模型仅输出识别自励系统并应用于燃气轮机燃烧室

• DOI: 10.1080/00207179.2022.2137702
• 发表时间: 2022
• 期刊: International Journal of Control
• 影响因子: 2.1
• 作者: Paredes, Juan A.;Yang, Yulong;Bernstein, Dennis S.
• 通讯作者: Bernstein, Dennis S.

Large-eddy simulation and challenges for projection-based reduced-order modeling of a gas turbine model combustor

• DOI: 10.1177/17568277221100650
• 发表时间: 2022-03
• 期刊: International Journal of Spray and Combustion Dynamics
• 影响因子: 1.6
• 作者: Nicholas Arnold-Medabalimi;Cheng Huang;K. Duraisamy

Design and Characterization of the Dual Independent Swirl Combustor Facility (DISCo)

双独立旋流燃烧室设施 (DISCo) 的设计和表征

• DOI: 10.2514/6.2021-3479
• 发表时间: 2021
• 期刊: AIAA Propulsion and Energy Forum
• 作者: Ramesh, Rahul;Obidov, Sanjar;Paredes, Juan;S. Bernstein, Dennis;Gamba, Mirko
• 通讯作者: Gamba, Mirko