Deep Model Predictive Control with Online Learning for Complex Physical Systems

Understand

The control of complex systems is of critical importance in many branches of science, engineering, and industry.

• Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy (e.g., wind, tidal, and combustion), transportation (e.g., planes, trains, and automobiles), security (e.g., tracking airborne contamination), and health (e.g., artificial hearts and artificial respiration).
• However, the high-dimensional, nonlinear, and multi-scale dynamics make real-time feedback control infeasible.
• Fortunately, these high-dimensional systems exhibit dominant, low-dimensional patterns of activity that can be exploited for effective control in the sense that knowledge of the entire state of a system is not required.