Journal ArticleDOI
Finite-horizon covariance control for discrete-time stochastic linear systems subject to input constraints
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TLDR
The main steps for the transcription of the covariance control problem is presented, which is originally formulated as a stochastic optimal control problem into a deterministic nonlinear program (NLP) with a convex performance index and with both convex and non-convex constraints.About:
This article is published in Automatica.The article was published on 2018-05-01. It has received 68 citations till now. The article focuses on the topics: Convex optimization & Stochastic control.read more
Citations
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Journal ArticleDOI
Optimal Covariance Control for Stochastic Systems Under Chance Constraints
TL;DR: In this paper, the covariance control problem with chance constraints was studied and a convex programming approach was proposed to solve the problem of covariance steering under chance constraints, which was shown to be equivalent to the one presented in this paper.
Journal ArticleDOI
Optimal Stochastic Vehicle Path Planning Using Covariance Steering
TL;DR: This letter develops an approach that is based on optimal covariance steering, which explicitly steers the state covariance for stochastic linear systems and verifies the proposed framework using extensive numerical simulations.
Journal ArticleDOI
Stochastic Control Liaisons: Richard Sinkhorn Meets Gaspard Monge on a Schrödinger Bridge
TL;DR: Schrodinger's problem represents an early example of a fundame... as discussed by the authors, which is an example of large deviations of the empirical distribution of a hot gas Gedanken experiment.
Proceedings ArticleDOI
Input Hard Constrained Optimal Covariance Steering
TL;DR: In this paper, the optimal covariance steering (OCS) problem for stochastic discrete linear systems with additive Gaussian noise under state chance constraints and input hard constraints is addressed.
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Reflected Schr\"odinger Bridge: Density Control with Path Constraints
TL;DR: This paper extends the theory of Schrödinger bridges to account the reflecting boundary conditions for the sample paths, and provides a computational framework building on the previous work on proximal recursions, to solve the minimum control effort density steering problem subject to state constraints.
References
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Book
Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
Book
Topics in Matrix Analysis
TL;DR: The field of values as discussed by the authors is a generalization of the field of value of matrices and functions, and it includes singular value inequalities, matrix equations and Kronecker products, and Hadamard products.
Book
Convex Optimization Algorithms
TL;DR: This book aims to provide a history of Balkan literature from 1989 to the present day in the context of the conflicts of the 1990s and beyond.
Journal ArticleDOI
Optimization over state feedback policies for robust control with constraints
TL;DR: It is shown that the class of admissible affine state feedback control policies with knowledge of prior states is equivalent to the classOf admissible feedback policies that are affine functions of the past disturbance sequence, which implies that a broad class of constrained finite horizon robust and optimal control problems can be solved in a computationally efficient fashion using convex optimization methods.
Proceedings ArticleDOI
A covariance control theory
A. F. Hotz,Robert E. Skelton +1 more
TL;DR: This paper introduces a theory for designing linear feedback controllers so that the closed loop system achieves a specified state covariance.