Integrated Direct and Indirect Adaptive Robust Control With Quantitative Robust Parameter Estimation -- Theory and Applications

今天的行业要求机器不仅要能生产出高质量的产品和高生产率，而且要有内置的智能，如预测和诊断功能，可以减少机器停机时间和维护成本。这些工业趋势对以性能为导向的先进控制提出了巨大的需求。此外，控制系统应具有实时学习能力，以帮助构建智能功能。这项拟议的研究是朝着满足这些工业需求迈出的一步。具体地说，该项目的目标是：(I)为设计新一代面向性能的非线性自适应鲁棒控制器(ARC)开发一个通用的理论框架，这些控制器实用、明确地考虑过程非线性和不确定性的影响，并具有准确的在线参数估计，用于辅助目的，如机器部件健康监测和预测；(Ii)将新的集成ARC设计应用于现代机械系统的智能和精密控制，例如高速电磁电机驱动的系统。所提出的集成ARC设计不仅具有重要的实用价值，而且通过弥合直接和间接两类不同的自适应控制设计之间的差距，为自适应控制领域做出基础性的理论贡献。所提出的定量稳健参数估计将产生深远的实际影响，因为它也是其他工业技术的关键推动因素，例如自动车载建模。这项拟议的研究涵盖了大多数大学教授的两个主要控制领域：非线性和自适应。拟议中的应用是工程专业学生在职业生涯中最有可能遇到的应用：液压驱动的重型机械和电机驱动的精密设备。研究结果将对未来的控制课程产生持久的影响，无论是本科生还是研究生。这项拟议的研究为新一代控制器的设计提供了坚实的基础，这些控制器将帮助工业制造高性能和高智能的现代机器。此外，NSF的支持将帮助PI继续目前的行业合作，并建立新的行业和国际合作，这从Maxtor Inc.的支持信中可见一斑。

Today's industry demands machines that will not only produce high quality products with high productivity, but also feature built-in intelligence like prognostic and diagnostic capabilities that can reduce machine down-times and maintenance costs. These industrial trends put a great demand on performance oriented advanced controls. Additionally, the control system should possess a real-time learning capability to aid the construction of intelligent features. The proposed research is a step toward meeting these industrial demands. Specifically, the objectives of the project are: (i) to develop a general theoretical framework for the design of a new generation of performance oriented nonlinear adaptive robust controllers (ARC) that are practical, explicitly take into account the effects of process nonlinearities and uncertainties, and have accurate on-line parameter estimation for secondary purposes such as machine component health monitoring and prognosis; (ii) to apply the new integrated ARC design to the intelligent and precision control of modern mechanical systems such as high-speed electro-magnetic motor driven ones. The proposed integrated ARC design will not only have significant practical value, but also make fundamental theoretical contributions to the adaptive control community by bridging the gap between the two distinct classes of adaptive control designs: direct and indirect. The proposed quantitative robust parameter estimation will have a profound practical impact since it is also a key enabler for other industrial technologies such as automated on-board modeling. The proposed research encompasses two major fields of controls that have been taught in most universities: nonlinear and adaptive. The proposed applications are those that engineering students will most likely encounter in their career: hydraulically actuated heavy machines and motor driven precision devices. The research results will have a lasting impact on future control curriculum, at both the undergraduate and the graduate level. The proposed research provides a solid foundation for the design of a new generation of controllers that will help industry build modern machines of great performance and intelligence. In addition, the NSF support will help the PI continue current industrial collaborations and to establish new industrial as well as international ones, as evident in the support letter from Maxtor Inc.