Timoshenko梁半离散格式的一致指数稳定性和控制收敛性

IF 1.7 2区 数学 Q2 MATHEMATICS, APPLIED
Fu Zheng, Zhen Jia, Bao-Zhu Guo
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引用次数: 0

摘要

本文研究边界控制下Timoshenko梁的数值逼近。已知边界反馈下的连续系统是指数稳定的。首先,将连续系统转化为等价的一阶port- hamilton公式。应用基本降阶有限差分格式导出一类半离散系统。其次,提出了一种全新的方法,该方法基于一个包含终态可观测性和精确可观测性的混合离散可观测性不等式来证明离散系统的一致指数稳定性。更有趣的是,离散系统稳定性的证明与连续系统稳定性的证明几乎是平行的。第三,利用Trotter-Kato定理证明了半离散系统的解对原系统的解具有强收敛性。最后,根据罗素的“稳定可控性”原理,证明了连续系统和离散系统的精确可控性,并推导了显式控制。此外,该方法首次证明了离散控制对连续控制的收敛性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uniform Exponential Stability and Control Convergence of Semi-discrete Scheme for a Timoshenko Beam

This paper considers numerical approximations of a Timoshenko beam under boundary control. The continuous system under boundary feedback is known to be exponentially stable. Firstly, the continuous system is transformed into an equivalent first-order port-Hamiltonian formulation. A basically order reduction finite difference scheme is applied to derive a family of semi-discretized systems. Secondly, a completely new method which is based on a mixed discrete observability inequality involving final state observability and exact observability is developed to prove the uniform exponential stability of the discrete systems. More interestingly, the proof for the stability of discrete systems is almost parallel to that of the continuous counterpart. Thirdly, the solutions of the semi-discretized systems are shown to be strongly convergent to the solution of the original system through Trotter-Kato theorem. Finally, both exact controllability of continuous system and the discrete systems are proved in light of Russell’s “controllability via stability” principle and the explicit controls are derived. Moreover, the discrete controls are shown in first time to be convergent to the continuous control by proposed approach.

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来源期刊
CiteScore
3.30
自引率
5.60%
发文量
103
审稿时长
>12 weeks
期刊介绍: The Applied Mathematics and Optimization Journal covers a broad range of mathematical methods in particular those that bridge with optimization and have some connection with applications. Core topics include calculus of variations, partial differential equations, stochastic control, optimization of deterministic or stochastic systems in discrete or continuous time, homogenization, control theory, mean field games, dynamic games and optimal transport. Algorithmic, data analytic, machine learning and numerical methods which support the modeling and analysis of optimization problems are encouraged. Of great interest are papers which show some novel idea in either the theory or model which include some connection with potential applications in science and engineering.
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