Distributed synchronization method of multi-motor driving system’s accelerated backstepping tracking control

IF 6.3 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
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引用次数: 0

Abstract

This paper proposes a distributed synchronization control method and an accelerated backstepping tracking control scheme for the multi-motor driving system (MMDS). In the first step, we create a dynamic model of the MMDS with complex nonlinear dynamics, encompassing elements such as the dead zone, frictions, and disturbances. Next, in order to tackle the challenge of load tracking, we fuse a speed function, a cosine barrier function, a second-order tracking differentiator (TD), and a disturbance compensator into the backstepping approach. Lastly, to address potential issues related to diverse torque inputs, which could result in the overload occurrences, we put forward a novel distributed synchronization control scheme. This scheme aims to achieve torque synchronization for the MMDS while simultaneously ensuring superior load tracking performance. In the distributed synchronization control, a communication network is built to achieve the local coupling and improve the synchronization efficiency, and a corresponding mean deviation coupling synchronization control scheme is designed. Lyapunov theory is utilized to demonstrate the stability of the introduced control scheme. The simulation experimental results for the MMDS show the effectiveness of the proposed scheme.

多电机驱动系统加速反步态跟踪控制的分布式同步方法
本文针对多电机驱动系统(MMDS)提出了分布式同步控制方法和加速反步进跟踪控制方案。首先,我们创建了具有复杂非线性动态特性的 MMDS 动态模型,其中包括死区、摩擦和干扰等元素。接下来,为了应对负载跟踪的挑战,我们将速度函数、余弦障碍函数、二阶跟踪微分器(TD)和干扰补偿器融合到反向步进方法中。最后,为解决可能导致过载的不同扭矩输入相关潜在问题,我们提出了一种新型分布式同步控制方案。该方案旨在实现 MMDS 的扭矩同步,同时确保卓越的负载跟踪性能。在分布式同步控制中,建立了一个通信网络来实现局部耦合,提高同步效率,并设计了相应的均值偏差耦合同步控制方案。利用李亚普诺夫理论证明了所引入控制方案的稳定性。MMDS 的仿真实验结果表明了所提方案的有效性。
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
ISA transactions
ISA transactions 工程技术-工程:综合
CiteScore
11.70
自引率
12.30%
发文量
824
审稿时长
4.4 months
期刊介绍: ISA Transactions serves as a platform for showcasing advancements in measurement and automation, catering to both industrial practitioners and applied researchers. It covers a wide array of topics within measurement, including sensors, signal processing, data analysis, and fault detection, supported by techniques such as artificial intelligence and communication systems. Automation topics encompass control strategies, modelling, system reliability, and maintenance, alongside optimization and human-machine interaction. The journal targets research and development professionals in control systems, process instrumentation, and automation from academia and industry.
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