Optimal regulator for a class of nonlinear stochastic systems with random coefficients

IF 2.5 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS European Journal of Control Pub Date : 2023-11-01 DOI:10.1016/j.ejcon.2023.100844

Mashael Algoulity , Bujar Gashi

引用次数: 1

Abstract

We consider an optimal regulator problem for a class of nonlinear stochastic systems with a square-root nonlinearity and random coefficients, and using the quadratic-linear criterion. This represents a certain nonlinear generalisation of the stochastic linear-quadratic control problem with random coefficients. The solution if found in an explicit closed-form as an affine state-feedback control in terms of a Riccati and linear backward stochastic differential equations. As an application, we give the solution to an optimal investment problem in a market with random coefficients.

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一类具有随机系数的非线性随机系统的最优调节器

考虑一类具有平方根非线性和随机系数的非线性随机系统的最优调节器问题，并使用二次线性准则。这代表了具有随机系数的随机线性二次控制问题的某种非线性推广。该问题的解以显式封闭形式表示为仿射状态反馈控制，其形式为Riccati和线性倒向随机微分方程。作为一个应用，我们给出了具有随机系数市场的最优投资问题的解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

European Journal of Control 工程技术-自动化与控制系统

CiteScore

5.80

自引率

5.90%

发文量

131

审稿时长

1 months

期刊介绍： The European Control Association (EUCA) has among its objectives to promote the development of the discipline. Apart from the European Control Conferences, the European Journal of Control is the Association''s main channel for the dissemination of important contributions in the field. The aim of the Journal is to publish high quality papers on the theory and practice of control and systems engineering. The scope of the Journal will be wide and cover all aspects of the discipline including methodologies, techniques and applications. Research in control and systems engineering is necessary to develop new concepts and tools which enhance our understanding and improve our ability to design and implement high performance control systems. Submitted papers should stress the practical motivations and relevance of their results. The design and implementation of a successful control system requires the use of a range of techniques: Modelling Robustness Analysis Identification Optimization Control Law Design Numerical analysis Fault Detection, and so on.

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