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相关论文: Linear Quadratic Dual Control

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An autonomous and resilient controller is proposed for leader-follower multi-agent systems under uncertainties and cyber-physical attacks. The leader is assumed non-autonomous with a nonzero control input, which allows changing the team…

多智能体系统 · 计算机科学 2018-04-10 Rohollah Moghadam , Hamidreza Modares

This paper considers the Linear Quadratic Regulator problem for linear systems with unknown dynamics, a central problem in data-driven control and reinforcement learning. We propose a method that uses data to directly return a controller…

系统与控制 · 电气工程与系统科学 2020-05-05 Claudio De Persis , Pietro Tesi

Linear-quadratic optimal control problem for systems governed by forward-backward stochastic differential equations has been extensively studied over the past three decades. Recent research has revealed that for forward-backward control…

最优化与控制 · 数学 2025-04-22 Qi Lü , Bowen Ma , Hanxiao Wang

Distributed optimal control is known to be challenging and can become intractable even for linear-quadratic regulator problems. In this work, we study a special class of such problems where distributed state feedback controllers can give…

系统与控制 · 电气工程与系统科学 2024-03-14 Johan Olsson , Runyu Zhang , Emma Tegling , Na Li

In this paper, we present a novel method for computing the optimal feedback gain of the infinite-horizon Linear Quadratic Regulator (LQR) problem via an ordinary differential equation. We introduce a novel continuous-time Bellman error,…

系统与控制 · 电气工程与系统科学 2026-04-17 Armin Gießler , Albertus Johannes Malan , Sören Hohmann

In this article we study the optimal control problem with quadratic functionals for a linear Volterra integro-differential equation in Hilbert spaces. With the finite history seen as an (additional) initial datum for the evolution,…

最优化与控制 · 数学 2023-03-10 Paolo Acquistapace , Francesca Bucci

A supervised learning approach for the solution of large-scale nonlinear stabilization problems is presented. A stabilizing feedback law is trained from a dataset generated from State-dependent Riccati Equation solves. The training phase is…

最优化与控制 · 数学 2021-03-09 Giacomo Albi , Sara Bicego , Dante Kalise

We present the stability analysis for the new regulation-triggered approach to adaptive control introduced in a companion paper. Due to the fact that the closed-loop system is hybrid, our proofs have essential differences from the…

最优化与控制 · 数学 2016-09-13 Iasson Karafyllis , Miroslav Krstic

This paper presents an online learning-based adaptive control framework for density-matrix tracking in a two-level Lindblad-Gorini-Kossakowski-Sudarshan (LGKS) quantum system, in which the feedback control law does not require prior…

最优化与控制 · 数学 2026-02-25 Jhon Manuel Portella Delgado , Ankit Goel

Feedback control problems involving autonomous quadratic systems are prevalent, yet there are only a limited number of software tools available for approximating their solution due to the complexity of the problem. This paper represents a…

最优化与控制 · 数学 2019-10-09 Jeff Borggaard , Lizette Zietsman

The main challenge for adaptive regulation of linear-quadratic systems is the trade-off between identification and control. An adaptive policy needs to address both the estimation of unknown dynamics parameters (exploration), as well as the…

系统与控制 · 计算机科学 2019-04-01 Mohamad Kazem Shirani Faradonbeh , Ambuj Tewari , George Michailidis

In this paper, the open-loop, closed-loop, and weak closed-loop solvability for discrete-time linear-quadratic (LQ) control problem is considered due to the fact that it is always open-loop optimal solvable if the LQ control problem is…

最优化与控制 · 数学 2025-02-18 Yue Sun , Xianping Wu , Xun Li

In this paper, we directly design a state feedback controller that stabilizes a class of uncertain nonlinear systems solely based on input-state data collected from a finite-length experiment. Necessary and sufficient conditions are derived…

系统与控制 · 电气工程与系统科学 2021-03-30 Alessandro Luppi , Claudio De Persis , Pietro Tesi

In this paper we study the exact null-controllability property for a class of controlled PDMP of switch type with switch-dependent, piecewise linear dynamics and multiplicative jumps. First, we show that exact null-controllability induces a…

最优化与控制 · 数学 2016-10-07 Dan Goreac

We study the closed-loop solvability of a stochastic linear quadratic optimal control problem for systems governed by stochastic evolution equations. This solvability is established by means of solvability of the corresponding Riccati…

最优化与控制 · 数学 2019-01-21 Qi Lü

The stabilizability of a general class of abstract parabolic-like equations is investigated, with a finite number of actuators. This class includes the case of actuators given as delta distributions located at given points in the spatial…

最优化与控制 · 数学 2023-08-21 Karl Kunisch , Sérgio S. Rodrigues , Daniel Walter

This paper investigates the $H_{2}/H_{\infty}$ control problem for linear stochastic differential systems under partial observation. Unlike existing studies that assume full state accessibility, we consider the scenario where the controller…

最优化与控制 · 数学 2026-04-24 Changwang Xiao , Nan Yang , Qingxin Meng

The problem of data-driven control is addressed here in the context of switched affine systems. This class of nonlinear systems is of particular importance when controlling many types of applications in electronic, biology, medicine, etc.…

系统与控制 · 电气工程与系统科学 2023-02-24 Alexandre Seuret , Carolina Albea , Francisco Gordillo

We study model-free learning methods for the output-feedback Linear Quadratic (LQ) control problem in finite-horizon subject to subspace constraints on the control policy. Subspace constraints naturally arise in the field of distributed…

系统与控制 · 电气工程与系统科学 2021-07-14 Luca Furieri , Yang Zheng , Maryam Kamgarpour

This paper studies the linear quadratic regulator (LQR) problem over an unknown Bernoulli packet loss channel. The unknown loss rate is estimated using finite channel samples and a certainty-equivalence (CE) optimal controller is then…

系统与控制 · 电气工程与系统科学 2025-06-17 Zhenning Zhang , Liang Xu , Yilin Mo , Xiaofan Wang