Dynamic events-based adaptive NN output feedback control of interconnected nonlinear systems under general output constraint

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rui Meng , Changchun Hua , Kuo Li , Qidong Li
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引用次数: 0

Abstract

This paper investigates the adaptive NN output feedback tracking control problem for a class of interconnected nonlinear systems. Unlike the existing control algorithms, we propose a dynamic event-triggered output constraint control algorithm.First, a reduced-order dynamic gain K-filter is established to construct the unmeasurable state variables. Second, an asymmetric constraint function with a special time-varying function is proposed, which can handle the case where the initial values of the constraint boundaries are unlimited. Then, a dynamic event-triggered mechanism based on the arctangent function is developed, which avoids the continuous transmission of control signals. With the help of the Lyapunov stability theory, it is rigorously proved that all signals of the closed-loop systems are bounded and the tracking error satisfies the output constraint requirement.Finally, the validity of the proposed algorithm is justified by the use of a numerical simulation.
一般输出约束条件下互联非线性系统的基于动态事件的自适应 NN 输出反馈控制
研究了一类互联非线性系统的自适应神经网络输出反馈跟踪控制问题。与现有的控制算法不同,我们提出了一种动态事件触发输出约束控制算法。首先,建立了一个降阶动态增益k滤波器来构造不可测状态变量。其次,提出了一种具有特殊时变函数的非对称约束函数,可以处理约束边界初值无限的情况;然后,提出了一种基于反正切函数的动态事件触发机制,避免了控制信号的连续传递。借助李雅普诺夫稳定性理论,严格证明了闭环系统的所有信号都是有界的,跟踪误差满足输出约束要求。最后,通过数值仿真验证了所提算法的有效性。
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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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