An Experimental Study of the Empirical Identification Method to Infer an Unknown System Transfer Function

IF 2.9 Q2 ROBOTICS

Robotics Pub Date : 2023-10-09 DOI:10.3390/robotics12050140

Jacob Gonzalez-Villagomez, Esau Gonzalez-Villagomez, Carlos Rodriguez-Donate, Eduardo Cabal-Yepez, Luis Manuel Ledesma-Carrillo, Geovanni Hernández-Gómez

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Abstract

Identification is considered a very important procedure, within the control area, to estimate the best-possible approximate model among different designs. Its significance comes from the fact that more than 75% of the cost associated with an advanced control project is aimed at obtaining a precise mathematical modeling. Therefore, in this work, an exhaustive analysis was carried out to determine the appropriate input stimulus for an unknown real system that must be controlled, with the aim of accurately estimating its transfer function (TF) using the empirical identification method (gray-box). The analysis was performed quantitatively by means of three tests: (i) the PID controller step response was evaluated theoretically; (ii) the controller performance was assessed in a Cartesian robot by tracking a trajectory defined through a Gaussian acceleration profile; (iii) the efficiency of the determined input stimulus with the best performance on inferring the TF for the system to be controlled was verified by assessing its operation in a real system, through repeatability tests, utilizing the integral errors.

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基于经验辨识方法推断未知系统传递函数的实验研究

识别被认为是一个非常重要的过程，在控制区域内，以估计不同设计之间的最佳可能近似模型。它的重要性在于，与先进控制项目相关的75%以上的成本都是为了获得精确的数学模型。因此，在这项工作中，我们进行了详尽的分析，以确定必须控制的未知真实系统的适当输入刺激，目的是使用经验识别方法(灰盒)准确估计其传递函数(TF)。通过三个测试进行定量分析:(i)从理论上评估了PID控制器的阶跃响应;(ii)通过跟踪由高斯加速度轮廓定义的轨迹来评估笛卡尔机器人的控制器性能;(iii)利用积分误差的可重复性测试，通过评估其在真实系统中的运行情况，验证了在推断待控制系统的TF方面具有最佳性能的确定输入刺激的效率。

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

Robotics Mathematics-Control and Optimization

CiteScore

6.70

自引率

8.10%

发文量

114

审稿时长

11 weeks

期刊介绍： Robotics publishes original papers, technical reports, case studies, review papers and tutorials in all the aspects of robotics. Special Issues devoted to important topics in advanced robotics will be published from time to time. It particularly welcomes those emerging methodologies and techniques which bridge theoretical studies and applications and have significant potential for real-world applications. It provides a forum for information exchange between professionals, academicians and engineers who are working in the area of robotics, helping them to disseminate research findings and to learn from each other’s work. Suitable topics include, but are not limited to: -intelligent robotics, mechatronics, and biomimetics -novel and biologically-inspired robotics -modelling, identification and control of robotic systems -biomedical, rehabilitation and surgical robotics -exoskeletons, prosthetics and artificial organs -AI, neural networks and fuzzy logic in robotics -multimodality human-machine interaction -wireless sensor networks for robot navigation -multi-sensor data fusion and SLAM