Nonlinear Uncertainty Control with Iterative Covariance Steering
Optimization and Control
2019-09-16 v2
Abstract
This paper considers the problem of steering the state distribution of a nonlinear stochastic system from an initial Gaussian to a terminal distribution with a specified mean and covariance, subject to probabilistic path constraints. An algorithm is developed to solve this problem by iteratively solving an approximate linearized problem as a convex program. This method, which we call iterative covariance steering (iCS), is numerically demonstrated by controlling a double integrator with quadratic drag force subject to additive Brownian noise while satisfying probabilistic path constraints.
Cite
@article{arxiv.1903.10919,
title = {Nonlinear Uncertainty Control with Iterative Covariance Steering},
author = {Jack Ridderhof and Kazuhide Okamoto and Panagiotis Tsiotras},
journal= {arXiv preprint arXiv:1903.10919},
year = {2019}
}