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In this paper, we present a scalable deep learning approach to solve opinion dynamics stochastic optimal control problems with mean field term coupling in the dynamics and cost function. Our approach relies on the probabilistic…

Multiagent Systems · Computer Science 2022-04-19 Tianrong Chen , Ziyi Wang , Evangelos A. Theodorou

The $H_\infty$ control design problem is considered for nonlinear systems with unknown internal system model. It is known that the nonlinear $ H_\infty $ control problem can be transformed into solving the so-called Hamilton-Jacobi-Isaacs…

Systems and Control · Computer Science 2014-05-13 Biao Luo , Huai-Ning Wu , Tingwen Huang

This paper is concerned with optimal control of stochastic fully coupled forward-backward linear quadratic (FBLQ) problems with indefinite control weight costs. In order to obtain the state feedback representation of the optimal control, we…

Optimization and Control · Mathematics 2019-02-27 Mingshang Hu , Shaolin Ji , Xiaole Xue

H{\infty} control of nonlinear continuous-time system depends on the solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which has been proved impossible to obtain a closed-form solution due to the nonlinearity of HJI equation. In order…

Systems and Control · Electrical Eng. & Systems 2024-03-20 Qi Wang

This paper presents a state- and control-dependent moving-horizon estimation (SCD-MHE) algorithm for nonlinear discrete-time systems. Within this framework, a pseudo-linear representation of nonlinear dynamics is leveraged utilizing state-…

Systems and Control · Electrical Eng. & Systems 2026-04-03 Mohammadreza Kamaldar

A method is presented for solving the discrete-time finite-horizon Linear Quadratic Regulator (LQR) problem subject to auxiliary linear equality constraints, such as fixed end-point constraints. The method explicitly determines an affine…

Systems and Control · Computer Science 2018-09-18 Forrest Laine , Claire Tomlin

Following the recent resurgence in establishing linear control theoretic benchmarks for reinforcement leaning (RL)-based policy optimization (PO) for complex dynamical systems with continuous state and action spaces, an optimal control…

Systems and Control · Electrical Eng. & Systems 2023-06-30 Leilei Cui , Lekan Molu

This paper considers a non-Markov control problem arising in a financial market where asset returns depend on hidden factors. The problem is non-Markov because nonlinear filtering is required to make inference on these factors, and hence…

Mathematical Finance · Quantitative Finance 2018-07-24 Andrew Papanicolaou

This report concerns the inverse problem of estimating a spacially dependent coefficient of a partial differential equation from observations of the solution at the boundary. Such a problem can be formulated as an optimal control problem…

Optimization and Control · Mathematics 2008-09-23 Jesper Carlsson

The control of relaxation-type systems of ordinary differential equations is investigated using the Hamilton-Jacobi-Bellman equation. First, we recast the model as a singularly perturbed dynamics which we embed in a family of controlled…

Optimization and Control · Mathematics 2024-04-23 Michael Herty , Hicham Kouhkouh

In this article a special class of nonlinear optimal control problems involving a bilinear term in the boundary condition is studied. These kind of problems arise for instance in the identification of an unknown space-dependent Robin…

Numerical Analysis · Mathematics 2024-12-20 Max Winkler

This paper proposes a fully data-driven approach for optimal control of nonlinear control-affine systems represented by a stochastic diffusion. The focus is on the scenario where both the nonlinear dynamics and stage cost functions are…

Optimization and Control · Mathematics 2025-11-03 Nicolas Hoischen , Petar Bevanda , Stefan Sosnowski , Sandra Hirche , Boris Houska

In this paper, we first establish the dynamic programming principle for stochastic optimal control problems defined on compact Riemannian manifolds without boundary. Subsequently, we derive the associated Hamilton-Jacobi-Bellman (HJB)…

Optimization and Control · Mathematics 2025-07-03 Dingqian Gao , Qi Lü

This paper addresses the mean-square optimal control problem for \a class of discrete-time linear systems with a quasi-colored control-dependent multiplicative noise via output feedback. The noise under study is novel and shown to have…

Systems and Control · Electrical Eng. & Systems 2021-09-06 Junhui Li , Jieying Lu , Weizhou Su

We devise an a posteriori error estimator for an affine optimal control problem subject to a semilinear elliptic PDE and control constraints. To approximate the problem, we consider a semidiscrete scheme based on the variational…

Optimization and Control · Mathematics 2025-05-08 Francisco Fuica , Nicolai Jork

In this paper we study stochastic control problems with delayed information, that is, the control at time $t$ can depend only on the information observed before time $t-H$ for some delay parameter $H$. Such delay occurs frequently in…

Probability · Mathematics 2018-08-23 Yuri F. Saporito , Jianfeng Zhang

In this paper, we consider the problem of distributed optimal control of linear dynamical systems with a quadratic cost criterion. We study the case of output feedback control for two interconnected dynamical systems, and show that the…

Optimization and Control · Mathematics 2012-04-18 Ather Gattami , Omid Khorsand

In this paper, we are concerned with the classical solvability of a class of second-order Hamilton-Jacobi-Bellman equations (HJB equations) arising from stochastic optimal control problems with linear dynamics and uniformly convex cost…

Optimization and Control · Mathematics 2025-12-19 Jinghua Li , Zhiyong Yu

This paper establishes the existence, uniqueness, and global $C^{1,\beta}$ regularity of positive classical solutions to a class of quasilinear Hamilton--Jacobi--Bellman (HJB) equations with Dirichlet boundary conditions on bounded convex…

Analysis of PDEs · Mathematics 2026-03-10 Dragos-Patru Covei

High-dimensional stochastic optimal control (SOC) becomes harder with longer planning horizons: existing methods scale linearly in the horizon $T$, with performance often deteriorating exponentially. We overcome these limitations for a…

Machine Learning · Computer Science 2026-03-25 Louis Claeys , Artur Goldman , Zebang Shen , Niao He
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