具有未知控制方向的非线性系统的隧道规定控制。

IF 10.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE transactions on neural networks and learning systems Pub Date : 2023-10-17 DOI:10.1109/TNNLS.2023.3322161
Ruihang Ji, Dongyu Li, Shuzhi Sam Ge, Haizhou Li
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引用次数: 0

摘要

本文解决了入口捕获问题(ECP),因此对于任何初始跟踪错误,都可以在用户给定的时间内将其调整为规定的性能约束。对于未知控制方向和非对称性能约束下的非线性系统,挑战在于如何消除初始条件限制和处理ECP。为了获得更好的跟踪性能,我们提出了一种统一的隧道规定性能(TPP),提供严格和严密的允许集。在比例函数的帮助下,开发了误差自校正函数(ESF),使控制方案适用于任何初始条件(包括初始约束违反),其中ESF的初始值总是满足性能约束。代替Nussbaum技术,引入了一个方向函数来处理未知的控制方向,同时这种方法能够减少控制峰值问题。使用ESF,连同TPP和定向函数,所得到的隧道规定控制(TPC)导致了底层ECP的解决方案,其也表现出低复杂性水平,因为不需要命令过滤器或动态表面控制。最后,仿真结果进一步证明了这些理论发现。
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Tunnel Prescribed Control of Nonlinear Systems With Unknown Control Directions.

This article solves the entry capture problem (ECP) such that for any initial tracking error, it can be regulated into the prescribed performance constraints within a user-given time. The challenge lies in how to remove the initial condition limitation and to handle the ECP for nonlinear systems under unknown control directions and asymmetric performance constraints. For better tracking performance, we propose a unified tunnel prescribed performance (TPP) providing strict and tight allowable set. With the aid of a scaling function, error self-tuning functions (ESFs) are then developed to make the control scheme suitable to any initial condition (including the initial constraint violation), where the initial values of ESFs always satisfy performance constraints. In lieu of the Nussbaum technique, an orientation function is introduced to deal with unknown control directions while such way is capable of reducing the control peaking problem. Using ESFs, together with TPP and an orientation function, the resulted tunnel prescribed control (TPC) leads to a solution for the underlying ECP, which also exhibits a low complexity level since no command filters or dynamic surface control is required. Finally, simulation results are provided to further demonstrate these theoretical findings.

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来源期刊
IEEE transactions on neural networks and learning systems
IEEE transactions on neural networks and learning systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, HARDWARE & ARCHITECTURE
CiteScore
23.80
自引率
9.60%
发文量
2102
审稿时长
3-8 weeks
期刊介绍: The focus of IEEE Transactions on Neural Networks and Learning Systems is to present scholarly articles discussing the theory, design, and applications of neural networks as well as other learning systems. The journal primarily highlights technical and scientific research in this domain.
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