Adaptive neural prescribed performance control for switched pure-feedback non-linear systems with input quantization

IF 1.9 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS
Zhong Cao, L. Zhang, A. Ahmad, F. Alsaadi, M. Alassafi
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引用次数: 9

Abstract

Purpose This paper aims to investigate an adaptive prescribed performance control problem for switched pure-feedback non-linear systems with input quantization. Design/methodology/approach By using the semi-bounded continuous condition of non-affine functions, the controllability of the system can be guaranteed. Then, a constraint variable method is introduced to ensure that the tracking error satisfies the prescribed performance requirements. Meanwhile, to avoid the design difficulties caused by the input quantization, a non-linear decomposition method is adopted. Finally, the feasibility of the proposed control scheme is verified by a numerical simulation example. Findings Based on neural networks and prescribed performance control method, an adaptive neural control strategy for switched pure-feedback non-linear systems is proposed. Originality/value The complex deduction and non-differentiable problems of traditional prescribed performance control methods can be solved by using the proposed error transformation approach. Besides, to obtain more general results, the restrictive differentiability assumption on non-affine functions is removed.
带输入量化的开关纯反馈非线性系统的自适应神经预定性能控制
目的研究具有输入量化的切换纯反馈非线性系统的自适应预定性能控制问题。设计/方法/途径利用非仿射函数的半有界连续条件,可以保证系统的可控性。然后,引入了一种约束变量方法,以确保跟踪误差满足规定的性能要求。同时,为了避免输入量化带来的设计困难,采用了非线性分解方法。最后,通过数值仿真实例验证了所提控制方案的可行性。基于神经网络和规定的性能控制方法,提出了一种切换纯反馈非线性系统的自适应神经控制策略。独创性/价值使用所提出的误差变换方法可以解决传统规定性能控制方法的复杂推导和不可微问题。此外,为了获得更一般的结果,去掉了非仿射函数上的限制性可微性假设。
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
Assembly Automation
Assembly Automation 工程技术-工程:制造
CiteScore
4.30
自引率
14.30%
发文量
51
审稿时长
3.3 months
期刊介绍: Assembly Automation publishes peer reviewed research articles, technology reviews and specially commissioned case studies. Each issue includes high quality content covering all aspects of assembly technology and automation, and reflecting the most interesting and strategically important research and development activities from around the world. Because of this, readers can stay at the very forefront of industry developments. All research articles undergo rigorous double-blind peer review, and the journal’s policy of not publishing work that has only been tested in simulation means that only the very best and most practical research articles are included. This ensures that the material that is published has real relevance and value for commercial manufacturing and research organizations.
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