Dr. John W. Simpson-Porco

Achieving reference tracking and disturbance rejection in the presence of model uncertainty are two of the fundamental goals of feedback design. In applications such as grid-level power systems control, the plant is highly uncertain but stable, and there is a long practical history of 'low-gain' design principles being leveraged to design robust output regulation loops. In addition to traditional regulation goals, higher-level specifications such as optimal operation and satisfaction of operational constraints are of significant interest. 

This talk will outline some of our recent advances in low-gain output regulation design for linear and nonlinear systems. We begin in the nonlinear case, focusing on low-gain integral control in both continuous and discrete-time, and provide generalized conditions based on contraction theory for closed-loop stability. The results are applied to provide the first rigorous stability guarantees for automatic generation control in large-scale bulk power systems. We next present a framework for optimal steady-state control, which is an extension of traditional asymptotic output regulation to include equilibrium optimality criterion and constraint satisfaction. Several fully constructive and practical designs are presented for linear time-invariant systems, with stability guarantees following from the previous integral control results. Time permitting, we revisit the tuning regulator concept proposed by E. J. Davison in 1976, which is a framework for low-order and nearly-model-free output regulation design, considering time-varying disturbances. We improve upon the original design by (i) providing more systematic pole-placement design procedures, and (ii) reducing the large the number of online tuning parameters to a single scalar. We conclude with an outlook on the fusion of such traditional control ideas with modern data-driven design methods.

Biography:

Dr. Simpson-Porco is an Assistant Professor in the Edward S. Rogers Sr. Department Electrical and Computer Engineering at the University of Toronto. His research focuses on feedback control theory and applications of control and optimization in power and energy systems. John received his B.Sc. degree in Engineering Physics from Queen's University in 2010, and his PhD in Mechanical Engineering from the University of California, Santa Barbara in 2015. He was previously an Assistant Professor of Electrical and Computer Engineering at the University of Waterloo, Waterloo, Canada. Prof. Simpson-Porco is a recipient of the Automatica Paper Prize, the Center for Control, Dynamical Systems and Computation (CCDC) Best Thesis Award, and the IEEE PES Technical Committee Working Group Recognition Award for Outstanding Technical Report. He is currently an Associate Editor for the IEEE Transactions on Smart Grid.