Machine Learning-Based Multiagent Control for a Bunch of Flexible Robots

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Complexity Pub Date : 2024-04-20 DOI:10.1155/2024/1330458
Jun Wang, Jiali Zhang, Jafar Tavoosi, Mohammadamin Shirkhani
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引用次数: 0

Abstract

In this paper, two novel methodologies of employing machine learning (here, the type-2 fuzzy system) are presented to control a multiagent system in which the agents are flexible joint robots. In the previous methods, the static mode controller has been investigated, which has little flexibility and cannot measure all the states of the system, but in the method presented in this paper, we can eliminate these disadvantages. The control signal is consisting of feedback from the output and the estimated states of the system. In the first method, the control signal coefficients are calculated from the linear matrix inequality (LMI), followed by a type-2 fuzzy system that adds the compensation signal to the control signal. In the second method, the type-2 fuzzy system is directly used to estimate the control signal coefficients which do not employ LMI. Both methods have their disadvantages and benefits, so in general, one of these two methods cannot be considered superior. To prove the effectiveness of the two proposed methods, a topology with four robots has been considered. Both proposed methods have been evaluated for controlling the angle and speed of the robot link. Also, another simulation was made without using the fuzzy system to verify the importance of our methods. Simulation results indicate the proper efficiency of proposed methods, especially in presence of uncertainty in the system.

基于机器学习的灵活机器人多代理控制
本文提出了两种利用机器学习(这里指 2 型模糊系统)控制多代理系统的新方法,其中代理是灵活的关节机器人。在以往的方法中,我们研究的是静态模式控制器,这种控制器灵活性差,而且无法测量系统的所有状态,但在本文介绍的方法中,我们可以消除这些缺点。控制信号由输出反馈和系统估计状态组成。在第一种方法中,控制信号系数是通过线性矩阵不等式(LMI)计算得出的,然后通过 2 型模糊系统将补偿信号添加到控制信号中。在第二种方法中,不使用线性矩阵不等式,而是直接使用 2 型模糊系统来估计控制信号系数。这两种方法各有利弊,因此一般来说,不能认为这两种方法中的一种更优越。为了证明这两种方法的有效性,我们考虑了一个有四个机器人的拓扑结构。对这两种建议的方法进行了评估,以控制机器人链接的角度和速度。此外,还在不使用模糊系统的情况下进行了另一次模拟,以验证我们方法的重要性。模拟结果表明,特别是在系统存在不确定性的情况下,建议的方法具有适当的效率。
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
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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