多连杆机器人系统(Robogymnast)各种控制器设计研究

Bdereddin Abdul samad, Mahmoud Mohamed, Fatih Anayi, Y. Melikhov
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引用次数: 2

摘要

提出了一种三连杆机器人体操运动员的控制与稳定性评估方法,并对其进行了研究。在研究中,采用常规配置的线性二次调节器,并与模糊逻辑线性二次调节器混合方法进行了比较。机器人体操运动员的设计是为了模仿人类的动作,即双手悬吊在高杆上,然后在仍然抓住高杆的情况下进行倒立。因此,该系统在手元件和“高杆”之间有一个安全的连接,高杆安装在滚珠轴承上,可以自由旋转。此外,在研究中,对系统的数学模型进行了线性化,研究了通过应用拉格朗日方程确定系统状态空间的方法。模糊逻辑线性二次型调节器控制器用于识别系统响应在实现时的稳定程度。研究了影响欠驱动三连杆机械体操运动员上摆控制的因素。利用MATLAB Simulink进行了系统仿真,分析了超调量、上升时间、沉降时间等因素对系统的影响。该研究的主要目的在于研究如何将线性二次调节器或带有线性二次调节器的模糊逻辑控制器(FLQR)应用于机器人体操运动员,并在五种情况下评估系统行为,即原始值,该值正负±25%,正负±50%。为了进一步评估所使用控制器的性能,将这里发现的结果与最近的模糊线性二次型调节器控制器文献中的结果进行比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Investigation of Various Controller Designs for Multi-Link Robotic System (Robogymnast)
An approach to controlling the three-link Robogymnast robotic gymnast and assessing stability is proposed and examined. In the study, a conventionally configured linear quadratic regulator is applied and compared with a fuzzy logic linear quadratic regulator hybrid approach for stabilising the Robogymnast. The Robogymnast is designed to replicate the movement of a human as they hang with both hands holding the high bar and then work to wing up into a handstand, still gripping the bar. The system, therefore has a securely attached link between the hand element and the ‘high bar’, which is mounted on ball bearings and can rotate freely. Moreover, in the study, a mathematical model for the system is linearised, investigating the means of determining the state space in the system by applying Lagrange’s equation. The fuzzy logic linear quadratic regulator controller is used to identify how far the system responses stabilise when it is implemented. This paper investigates factors affecting the control of swing-up in the underactuated three-link Robogymnast. Moreover, a system simulation using MATLAB Simulink is conducted to show the impact of factors including overshoot, rising, and settling time. The principal objective of the study lies in investigating how a linear quadratic regulator or fuzzy logic controller with a linear quadratic regulator (FLQR) can be applied to the Robogymnast, and to assess system behaviour under five scenarios, namely the original value, this value plus or minus ±25%, and plus or minus ±50%. In order to further assess the performance of the controllers used, a comparison is made between the outcomes found here and findings in the recent literature with fuzzy linear quadratic regulator controllers.
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