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

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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.