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Asymptotic stability in economic receding horizon control can be obtained under a strict dissipativity assumption, related to positive-definiteness of a so-called rotated cost, and through the use of suitable terminal cost and constraints.…

系统与控制 · 电气工程与系统科学 2025-11-20 Mario Zanon

This paper is concerned with a stochastic linear-quadratic optimal control problem with regime switching, random coefficients, and cone control constraint. The randomness of the coefficients comes from two aspects: the Brownian motion and…

最优化与控制 · 数学 2022-01-07 Ying Hu , Xiaomin Shi , Zuo Quan Xu

We study a constrained stochastic control problem with jumps; the jump times of the controlled process are given by a Poisson process. The cost functional comprises quadratic components for an absolutely continuous control and the…

最优化与控制 · 数学 2013-04-29 Peter Kratz

The closed-loop stability and infinite-horizon performance of receding-horizon approximations are studied for non-stationary linear-quadratic regulator (LQR) problems. The approach is based on a lifted reformulation of the optimal control…

系统与控制 · 电气工程与系统科学 2023-09-06 Jintao Sun , Michael Cantoni

We consider the problem of stochastic optimal control, where the state-feedback control policies take the form of a probability distribution and where a penalty on the entropy is added. By viewing the cost function as a Kullback- Leibler…

最优化与控制 · 数学 2024-12-12 Marc Lambert , Francis Bach , Silvère Bonnabel

We study a finite-dimensional continuous-time optimal control problem on finite horizon for a controlled diffusion driven by Brownian motion, in the linear-quadratic case. We admit stochastic coefficients, possibly depending on an…

最优化与控制 · 数学 2016-09-19 Fulvia Confortola , Marco Fuhrman , Giuseppina Guatteri , Gianmario Tessitore

This paper considers linear-quadratic (LQ) stochastic leader-follower Stackelberg differential games for jump-diffusion systems with random coefficients. We first solve the LQ problem of the follower using the stochastic maximum principle…

最优化与控制 · 数学 2020-10-07 Jun Moon

This paper studies the learning-to-control problem under process and sensing uncertainties for dynamical systems. In our previous work, we developed a data-based generalization of the iterative linear quadratic regulator (iLQR) to design…

机器人学 · 计算机科学 2023-11-09 Ran Wang , Raman Goyal , Suman Chakravorty

This article explores the discrete-time stochastic optimal LQR control with delay and quadratic constraints. The inclusion of delay, compared to delay-free optimal LQR control with quadratic constraints, significantly increases the…

最优化与控制 · 数学 2024-11-19 Dawei Liu , Juanjuan Xu , huanshui Zhang

In this paper, a class of time inconsistent linear quadratic optimal control problems of mean-field stochastic differential equations (SDEs) is considered under Markovian framework. Open-loop equilibrium controls and their particular…

最优化与控制 · 数学 2018-02-06 Tianxiao Wang

The paper deals with a class of time-inconsistent control problems for McKean-Vlasov dynamics. By solving a backward time-inconsistent Hamilton-Jacobi-Bellman (HJB for short) equation coupled with a forward distribution-dependent stochastic…

最优化与控制 · 数学 2020-02-18 Hongwei Mei , Chao Zhu

This paper addresses the problem of learning the optimal control policy for a nonlinear stochastic dynamical system with continuous state space, continuous action space and unknown dynamics. This class of problems are typically addressed in…

机器学习 · 计算机科学 2019-04-18 Ran Wang , Karthikeya Parunandi , Dan Yu , Dileep Kalathil , Suman Chakravorty

This paper is concerned with a stochastic linear-quadratic (LQ) optimal control problem on infinite time horizon, with regime switching, random coefficients, and cone control constraint. To tackle the problem, two new extended stochastic…

最优化与控制 · 数学 2022-01-06 Ying Hu , Xiaomin Shi , Zuo Quan Xu

We propose a new risk-constrained formulation of the classical Linear Quadratic (LQ) stochastic control problem for general partially-observed systems. Our framework is motivated by the fact that the risk-neutral LQ controllers, although…

最优化与控制 · 数学 2021-12-15 Anastasios Tsiamis , Dionysios S. Kalogerias , Alejandro Ribeiro , George J. Pappas

We consider a stochastic control problem which is composed of a controlled stochastic differential equation, and whose associated cost functional is defined through a controlled backward stochastic differential equation. Under appropriate…

概率论 · 数学 2009-02-17 Rainer Buckdahn , Boubakeur Labed , Catherine Rainer , Lazhar Tamer

We consider a discrete-time Linear-Quadratic-Gaussian (LQG) control problem in which Massey's directed information from the observed output of the plant to the control input is minimized while required control performance is attainable.…

最优化与控制 · 数学 2017-06-13 Takashi Tanaka , Peyman Mohajerin Esfahani , Sanjoy K. Mitter

The stochastic linear--quadratic regulator problem subject to Gaussian disturbances is well known and usually addressed via a moment-based reformulation. Here, we leverage polynomial chaos expansions, which model random variables via series…

最优化与控制 · 数学 2025-02-14 Ruchuan Ou , Jonas Schießl , Michael Heinrich Baumann , Lars Grüne , Timm Faulwasser

We consider a class of $\ell_0$-regularized linear-quadratic (LQ) optimal control problems. This class of problems is obtained by augmenting a penalizing sparsity measure to the cost objective of the standard linear-quadratic regulator…

最优化与控制 · 数学 2015-07-31 MirSaleh Bahavarnia

In this paper, we focus on a class of time-inconsistent stochastic control problems, where the objective function includes the mean and several higher-order central moments of the terminal value of state. To tackle the time-inconsistency,…

数理金融 · 定量金融 2025-05-08 Yike Wang , Jingzhen Liu , Alain Bensoussan , Ka-Fai Cedric Yiu , Jiaqin Wei

We present an optimization-based approach to stochastic control problems with nonclassical information structures. We cast these problems equivalently as optimization prob- lems on joint distributions. The resulting problems are necessarily…

最优化与控制 · 数学 2013-09-17 Ankur A. Kulkarni , Todd P. Coleman
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