Adaptive discrete-time neural prescribed performance control: A safe control approach

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhonghua Wu , Bo Huang , Xiangwei Bu
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引用次数: 0

Abstract

Most existing results on prescribed performance control (PPC), subject to input saturation and initial condition limitations, focus on continuous-time nonlinear systems. This article, as regards discrete-time nonlinear systems, is dedicated to constructing a novel adaptive switching control strategy to circumvent the singular problem when the PPC undergoes input saturation, while the initial conditions of the system can be released under the framework of PPC. The main design steps and characteristics include: (1) By devising a new discrete-time global finite-time performance function (DTGFTPF), the constructed performance boundary is shown to survive insensitive to arbitrary initial values, which present in the system; (2) A discrete-time adaptive finite-time prescribed performance controller (DTAFPPC) and a discrete-time adaptive backstepping controller (DTABC) are constructed, simultaneously. The DTAFPPC possesses the capability to drive tracking error convergence within preset boundaries within a finite time. In the presence of input saturation, the DTABC is applied to prevent system instability while permitting tracking error to occasionally exceed performance bounds without compromising overall stability; and (3) To overcome non-causal problems inherent in backstepping designs, the current moment values of the errors are integrated into the controllers and the adaptive update laws. The stability of the closed-loop system is validated through Lyapunov analysis theory and simulations.
自适应离散时间神经规定性能控制:安全控制方法
现有的规定性能控制(PPC)研究大多集中在连续时间非线性系统上,受输入饱和和初始条件限制的影响。本文针对离散时间非线性系统,构建了一种新的自适应切换控制策略,以规避PPC输入饱和时的奇异性问题,同时在PPC框架下可以释放系统的初始条件。主要设计步骤和特点包括:(1)通过设计一个新的离散全局有限时间性能函数(DTGFTPF),构造的性能边界对系统中存在的任意初始值不敏感;(2)同时构造了离散时间自适应有限时间规定性能控制器(DTAFPPC)和离散时间自适应反演控制器(DTABC)。DTAFPPC具有在有限时间内驱动跟踪误差在预设边界内收敛的能力。在存在输入饱和的情况下,DTABC应用于防止系统不稳定,同时允许跟踪误差偶尔超过性能界限而不影响整体稳定性;(3)为了克服反步设计固有的非因果问题,将误差的当前矩值集成到控制器和自适应更新律中。通过李雅普诺夫分析理论和仿真验证了闭环系统的稳定性。
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
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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