This talk is concerned with the problem of designing a feedback controller for a multivariable system which regulates specified inputs and measurements to the solution of a steady-state optimization problem, despite modelling uncertainty and unmeasured exogenous disturbances. Specific instances of this problem arising in energy systems include the design of distributed architectures for frequency control in multi-area power systems, and the regulation of voltage levels in transmission and distribution systems in the presence of renewable penetration. We outline two constructive design frameworks for variants of this problem. The first framework is grounded in ideas from output regulator/servomechanism theory, and relies on inserting an appropriate "optimality model" into the feedback loop, analogous to the insertion of an internal model for asymptotic disturbance rejection problems. The second framework instead begins with projected gradient-type algorithms developed for convex optimization, adapts them to accept measurements, and leverages ideas from robust control to provide exponential stability certificates. We conclude with a look at some ongoing research, including extensions of the second framework to non-smooth composite optimization problems.
John W. Simpson-Porco is an Assistant Professor of Electrical and Computer Engineering at the University of Waterloo. His research focuses on feedback control theory and applications of control in modernized power grids. John received his B.Sc. degree in Engineering Physics from Queen's University in 2010 and his PhD degree in Mechanical Engineering from the University of California, Santa Barbara in 2015. He is a recipient of the Automatica Best Paper Prize and the Center for Control, Dynamical Systems and Computation Outstanding Scholar Fellowship and Best Thesis Award.