Partial-State Decomposition-Based Control of MIMO Nonminimum Phase Nonlinear Systems With Application to a Hypersonic Vehicle Model

IF 8.6 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Xinhao Zhang;Yanjun Zhang;Jianliang Ai;Yeguang Wang;Xuelin Zhang
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引用次数: 0

Abstract

This article proposes a stabilizing control scheme based on partial-state decomposition to exponentially stabilize multi-input and multioutput nonminimum phase nonlinear systems in a general normal form with a general relative degree. The scheme reveals that partial-state variables can play an essential role in stabilizing the whole system. Specifically, partial-state variables are decomposed into a sum of two vector signals s and N. Then, setting s as the output vector, a new normal form is derived. For the new normal form, N and an auxiliary signal are designed to exponentially stabilize the unstable zero/internal dynamics of the auxiliary system; and the real input is designed to ensure that $s $ is exponentially stable. In particular, the zero/internal dynamics dependence on the input is fully considered in this article, and the proposed method ensures global stabilization of the closed-loop system without relying on the existence condition of Lyapunov functions. Finally, a high fidelity hypersonic vehicle model is given to show the design procedure and verify the feasibility and validity of the proposed stabilizing control scheme.
本文提出了一种基于偏态分解的稳定控制方案,以指数方式稳定具有一般相对度的一般正态形式的多输入和多输出非最小相位非线性系统。该方案揭示了偏态变量在稳定整个系统中的重要作用。具体来说,部分状态变量被分解为两个矢量信号 s 和 N 之和。对于新的正态形式,N 和辅助信号的设计是为了指数稳定辅助系统不稳定的零点/内部动态;而实际输入的设计是为了确保 $s $ 是指数稳定的。特别是,本文充分考虑了零点/内部动力学对输入的依赖性,提出的方法无需依赖李亚普诺夫函数的存在条件就能确保闭环系统的全局稳定。最后,文章给出了一个高保真高超音速飞行器模型,以展示设计过程并验证所提稳定控制方案的可行性和有效性。
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
IEEE Transactions on Systems Man Cybernetics-Systems
IEEE Transactions on Systems Man Cybernetics-Systems AUTOMATION & CONTROL SYSTEMS-COMPUTER SCIENCE, CYBERNETICS
CiteScore
18.50
自引率
11.50%
发文量
812
审稿时长
6 months
期刊介绍: The IEEE Transactions on Systems, Man, and Cybernetics: Systems encompasses the fields of systems engineering, covering issue formulation, analysis, and modeling throughout the systems engineering lifecycle phases. It addresses decision-making, issue interpretation, systems management, processes, and various methods such as optimization, modeling, and simulation in the development and deployment of large systems.
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