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

控制技术使使用机器人制造和自动驾驶自动驾驶车辆成为可能。控制技术可提高生产力、效率和安全性。特别具有挑战性的应用程序要求计算机算法能够适应系统及其环境中不可预测的变化。该项目将使用自适应控制来改善燃烧过程中燃烧燃料的发动机的性能。这些发动机在全球范围内被用来为电网发电。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。根据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，并将增强在该技术领域工作的未来专业人员的多样性。该项目将通过开发回溯成本自适应控制(RCAC)的诊断建模技术，增进对反馈控制理论和实践的了解和理解。RCAC需要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精度(右半平面零点的位置和非线性的细节)。作为RCAC的扩展，自适应和闭环辨识作为双RCAC(DRCAC)并行执行。这一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识别未能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，以获取用于校准低保真模型的数据。DRCAC将使用这些模型进行在线分析和闭环仿真，并在必要时修改反馈传感和执行策略。该项目的智力目标是加深对双重控制的理解和诊断方法的发展，以便在理论和实践中促进复杂系统的自适应控制。

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.