Title: Distributed optimization and control with ALADIN
Structured nonlinear optimization problems arise in a variety of control applications ranging from nonlinear model predictive control via robust control for uncertain processes to distributed nonlinear control of hybrid systems. Recently, the Augmented Lagrangian based Alternating Direction Inexact Newton (ALADIN) method has been proposed to solve non-convex distributed optimization problems to local optimality. After reviewing the main idea of ALADIN, this talk focusses on three applications. The first one is about a real-time variant of ALADIN, which can be used to solve nonlinear model predictive control problems with long horizons. The performance of this real-time variant of ALADIN is illustrated by applying it to a continuously stirred tank reactor. The second application is about coordinating autonomous vehicles at traffic intersections. Finally, a third application of ALADIN in the field stochastic robust control is introduced, where an ensemble of uncertainty scenarios is optimized. We show how ALADIN can be used to robustly control an exothermic tubular plug flow reactor.
Yuning Jiang is a PhD student under the supervision of Prof. Boris Houska at the School of Information Science and Technology at ShanghaiTech University. He received his BS degree in Electrical Engineering from Shandong University in 2014. His research interests include numerical methods for distributed optimization and model predictive control.