Modelling and passive control of flexible guiding hoisting system with time-varying length

IF 1.8 4区数学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Mathematical and Computer Modelling of Dynamical Systems Pub Date : 2020-01-02 DOI:10.1080/13873954.2019.1699121

Naige Wang, Guohua Cao, Lu Yan, Lei Wang

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引用次数: 10

Abstract

ABSTRACT A coupled dynamic modelling of the flexible guiding hoisting system is established, which includes the transverse-longitudinal-coupled vibration and the rotational vibration. Substituting vibrational energy of the system into Hamilton principle and applying the dynamic constraint, a distributed parameter mathematical model of the multi-rope system is derived. It is governed by coupled partial differential equations and ordinary differential equations (PDEs-ODEs), where the dynamic constraint in the form of an unknown moving force is the only connection between the hoisting conveyance and the guiding ropes. Based on Galerkin method, the dynamic response of the system is validated by numerical calculation and ADAMS simulation. Besides, an absorber with artificial intelligence optimization is proposed to reduce system vibration. The simulation result has demonstrated that a hoisting conveyance resonance can be observed when the external disturbance frequency is close to the system natural frequencies. Moreover, a vibration absorber can effectively diminish the resonant peaks of the first three orders of the guiding rope.

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时变长度柔性导向提升系统建模与被动控制

摘要建立了柔性导向提升系统的耦合动力学模型，包括横向-纵向耦合振动和旋转振动。将系统的振动能量代入Hamilton原理，应用动力学约束，建立了多绳系统的分布参数数学模型。它由耦合偏微分方程和常微分方程（PDEs-ODE）控制，其中未知移动力形式的动态约束是起重运输和导向绳索之间的唯一连接。基于伽辽金方法，通过数值计算和ADAMS仿真验证了系统的动力响应。此外，还提出了一种基于人工智能优化的减振器来降低系统振动。仿真结果表明，当外部扰动频率接近系统固有频率时，可以观察到起重运输共振。此外，减振器可以有效地减小导绳的前三阶的共振峰。

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

Mathematical and Computer Modelling of Dynamical Systems 数学-计算机：跨学科应用

CiteScore

3.80

自引率

5.30%

发文量

审稿时长

>12 weeks

期刊介绍： Mathematical and Computer Modelling of Dynamical Systems (MCMDS) publishes high quality international research that presents new ideas and approaches in the derivation, simplification, and validation of models and sub-models of relevance to complex (real-world) dynamical systems. The journal brings together engineers and scientists working in different areas of application and/or theory where researchers can learn about recent developments across engineering, environmental systems, and biotechnology amongst other fields. As MCMDS covers a wide range of application areas, papers aim to be accessible to readers who are not necessarily experts in the specific area of application. MCMDS welcomes original articles on a range of topics including: -methods of modelling and simulation- automation of modelling- qualitative and modular modelling- data-based and learning-based modelling- uncertainties and the effects of modelling errors on system performance- application of modelling to complex real-world systems.