A Distributed Optimization Algorithm for Stochastic Optimal Control

TL;DR: This contribution proposes a heuristic for the computation of free final times of the sub-systems that allows the dynamic sub-processes to meet the constraints and demonstrates that the distributed methods converge to (local) optima.

TL;DR: In this article, a stochastic optimal tracking problem is formulated that can be expressed in function of the first two Stochastic moments of the state, which allows to penalize system performance and system robustness independently.

TL;DR: In this article , the authors propose an algorithm that calculates heuristically optimal solutions for dynamic optimization problems with path chance constraints, where the solution is a feasible point in the chance constraint sense and an optimal point of an approximated problem.

TL;DR: In this paper, the authors extended the ALADIN algorithm to non-convex continuous-time optimal control problems with nonlinear dynamics and linear coupling constraints, where the algorithm alternates between solving a convexified local problem and a linearized quadratic problem on a centralized entity.

TL;DR: This work represents the stochastic processes with an optimum trial basis from the Askey family of orthogonal polynomials that reduces the dimensionality of the system and leads to exponential convergence of the error.

TL;DR: In this paper, the authors propose an approach that attempts to make this trade-off more attractive by flexibly adjusting the level of conservatism of the robust solutions in terms of probabilistic bounds of constraint violations.

TL;DR: A condensing algorithm for the solution of the approximating linearly constrained quadratic subproblems, and high rank update procedures are introduced, which are especially suited for optimal control problems and lead to significant improvements of the convergence behaviour and reductions of computing time and storage requirements.