Bryan Keating


    • Resume
    • Masters Candidate (Expected Graduation – May 2016)
    • B.S. Mechanical Engineering – Cornell University, 2014
    • Research Interests: Real-time optimization, estimation, control




Ongoing Work

Developing models for online energy optimization of vapor compression systems can be prohibitively difficult and expensive. Recently, model-free adaptive extremum seeking control, a class of real-time optimizing control, has been proposed as an alternative to traditional first principles approaches. The goal of online optimization is to find economizing input values that minimize the plant’s operational cost. Often, the optimal value of these inputs is a function of the system’s disturbances. For systems with a direct measurement of the operational cost function and multiple process outputs, there is a wealth of information that can be exploited for online optimizing feedback control. When extremum seeking control is applied to these systems, multiple potential process measurements unrelated to achieving system performance objectives present a choice of the extremum seeking controlled variable. My research has focused on the use of a self-optimizing control analysis technique to choose the best controlled variable for a model-free extremum seeking controller. The approach combines extremum seeking’s ability to adapt to slowly varying disturbances under minimal assumptions about the system model with the transient performance guarantees provided by self-optimizing control.