存在不确定性的单连杆柔性关节机器人的模糊逻辑自整定PID控制

A. Dehghani, H. Khodadadi
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引用次数: 28

摘要

目前,柔性关节机器人(FJR)机械手在工业上得到了广泛的应用;然而,这些机器人有几个问题。这些问题都是在关节和与链接。另一个问题是它们复杂的动力学,这给机器人的控制带来了一些挑战。非线性、环间的相互作用和关节的柔性是导致这一问题的原因。本文主要研究如何提高这些机器人的跟踪性能。因此,在第一步,我们需要利用系统的物理关系,确定FJR的模型。本文将引入模糊逻辑自整定PID (FLST-PID)控制器,使FJR连杆的转角保持在理想位置。在经典的PID形式中,控制器的参数值Kp、Ki、Kd用许多不同的方法(如Ziegler-Nichols)计算,并且是常数。在FLST-PID中,参数值是通过模糊逻辑等智能方法计算出来的,并且在控制过程中会发生变化。为了证明所提出的控制器的能力,将为FJR设计一些经典的控制器,如PID, LQR和状态反馈,并将这些控制器与系统的响应进行比较。此外,考虑到系统参数的不确定性,将再次进行比较。仿真结果证实了所提出的控制器对系统具有最佳响应,特别是在不确定条件下。
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Fuzzy Logic Self-Tuning PID control for a single-link flexible joint robot manipulator in the presence of uncertainty
Nowadays, flexible joint robots (FJR) manipulators are widely used at industry; however, these robots have several problems. These problems are in the joint and with links. Another problem is their complex dynamics that make control of this robot have some challenges. Non-linearity, interaction between loops and flexibility in the joint cause this problem. The present paper has focused to improve the tracking performance of these robots. Therefore, at the first step, we need to use physical relations of system and determine a model for the FJR. In this paper, the Fuzzy Logic Self- Tuning PID (FLST-PID) controller will be introduced to keep the rotating angle of the link of FJR at desired position. In the classic PID forms, the parameter values of the controller i.e. Kp, Ki, Kd are calculated in many various methods like Ziegler-Nichols and are constant. In FLST-PID, the parameter values computed by intelligent methods like fuzzy logic and they vary during the controlling process. For demonstrating the ability of the proposed controller, some classic controller like PID, LQR and State Feedback will be designed for FJR and the response of the system with these controllers will be compared. Moreover, by considering some uncertainty on systems parameters, the comparison will be performed once again. Simulation results confirm the claims and show that the proposed controller has the best response for the system especially in uncertainty conditions.
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