A Distributed Optimization Algorithm for Stochastic Optimal Control. Issue 1 (July 2017)

• Record Type: Journal Article
• Authors: Jiang, Yuning; Nimmegeers, Philippe; Telen, Dries; Van Impe, Jan; Houska, Boris
• Is Part Of: IFAC-PapersOnLine. Volume 50:Issue 1(2017)
• Page Start: 11263
• Page End: 11268
• Publication Date: 2017-07
• Subjects: distributed optimal control -- stochastic optimal control -- sigma-point approach; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2017.08.1618
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: This paper presents a distributed non-convex optimization algorithm for solving stochastic optimal control problems to local optimality. Here, our focus is on a class of methods that approximates the probability distribution of the states of a stochastic optimal control problem with uncertain parameters by using a sigma point approach. This leads to a large but structured optimal control problem comprising a number of carefully selected uncertainty scenarios in order to enforce chance constraints. The approach achieves accuracies that are equivalent to a third order moment expansion. However, as the resulting large but structured optimal control problem is challenging to solve with existing numerical tools, this paper proposes a tailored distributed algorithm that exploits the particular structure that arises when applying the sigma point approach. The method is based on a tailored variant of the recently proposed augmented Lagrangian based alternating direction inexact Newton (ALADIN) algorithm. The approach is illustrated by the application to a benchmark case study involving a predator-prey-fishing model.