A Moving Horizon Approach to a Chance Constrained Nonlinear Dynamic Optimization Problem

Abstract

In this paper, a chance constrained nonlinear dynamic optimization problem is considered, which will be investigated by using a moving horizon scheme. In each horizon, the chance constraints will be written (transformed) in terms of those (input) random variables with known probability distributions by using monotonicity relations. Some definitions and properties related to the required monotonicity properties are introduced. For the application problem considered these monotonicity properties hold automatically true. The chance constraints and their gradients are evaluated by computing multivariate normal integrals using direct numerical integration. Numerical experimentation results will also be reported.