Title: Economic Optimization of Large-Scale Systems with Model Predictive Control
Abstract: Maintaining high standards of living while decreasing our impact on the planet requires both new technologies as well as increasingly efficient operation of these technologies at large scale. This talk presents the central ideas of model predictive control, which has become over the last 20 years the leading advanced feedback control method, both in industrial practice as well as a topic of control theory research. We discuss the fundamental reasons for this success, which builds upon the foundations of optimal control and dynamic modeling, supplemented with measurement feedback to make the resulting system robust against model inaccuracies and disturbances. We present a large scale example of such a system performing in real time an economic optimization of a 155 building campus-wide energy system.
Next we discuss the education of engineers to understand, operate and improve this kind of technology. We start with a motivational example that can be easily understood by any undergraduate engineering student. Although conceptually simple, the fundamental ideas of noisy measurements, unstable processes, Brownian motion, feedback stabilization, controller tuning, and integral control already play central roles in understanding the complex behavior that arises. The talk closes with two examples of recent experimental hardware advances that enable a rethinking of the control laboratory as a vehicle for both teaching and demonstrating new research results in feedback control theory.
Bio: James B. Rawlings received the B.S. from the University of Texas and the Ph.D. from the University of Wisconsin, both in Chemical Engineering. He spent one year at the University of Stuttgart as a NATO postdoctoral fellow and then joined the faculty at the University of Texas. He moved to the University of Wisconsin in 1995, and then to the University of California, Santa Barbara in 2018, and is currently the Mellichamp Process Control Chair in the Department of Chemical Engineering, and the co-director of the Texas-Wisconsin-California Control Consortium (TWCCC).
Professor Rawlings’s research interests are in the areas of chemical process modeling, monitoring and control, nonlinear model predictive control, moving horizon state estimation, and molecular-scale chemical reaction engineering. He has written numerous research articles and coauthored three textbooks: “Model Predictive Control: Theory Computation, and Design,” 2nd ed. (2020), with David Mayne and Moritz Diehl, “Modeling and Analysis Principles for Chemical and Biological Engineers” (2013), with Mike Graham, and “Chemical Reactor Analysis and Design Fundamentals,” 2nd ed. (2020), with John Ekerdt.
In recognition of his research and teaching, Professor Rawlings has
received several awards including:
He is a fellow of IFAC, IEEE, and AIChE.