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.
