{"title":"Design, simulation and comparison of controllers for a redundant robot","authors":"Claudio Urrea, John Kern","doi":"10.1016/j.csmssp.2015.12.001","DOIUrl":null,"url":null,"abstract":"<div><p>The simulation tools are the foundation for the design of robot systems, for the application of robots in complex environments and for the development of new control strategies and algorithms. Because of this, the design, simulation and comparison of the performance of controllers applied to a redundant robot with five degrees of freedom (DOF) are presented in this paper. Through homogeneous transformation matrices the inverse kinematic model of the redundant robot is obtained. Six controllers are prepared to test the robot’s dynamic model: hyperbolic sine–cosine; computed torque; sliding hyperbolic mode; control with learning; and adaptive. A simulation environment is developed by means of the MatLab/Simulink software, which allows analyzing the dynamic performance of the robot and of the designed controllers. This simulation environment is used to carry out different tests of the redundant manipulator model together with each controller as they are made to follow a trajectory in space. The results, obtained through a simulation environment, are represented by comparative curves and RMS indices of the joint and Cartesian errors, and they show that the redundant manipulator model follows the test trajectory with less pronounced maximum errors using the adaptive controller than the other controllers, with more homogeneous motions of the manipulator. The largest joint and Cartesian errors generated when testing the robot model, both in terms of maximum and RMS values, occurred when the computed torque controller is used. The results with that controller are obtained by executing three iterations for learning, because with more iterations the variations were not important.</p></div>","PeriodicalId":100220,"journal":{"name":"Case Studies in Mechanical Systems and Signal Processing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.csmssp.2015.12.001","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Case Studies in Mechanical Systems and Signal Processing","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2351988615300130","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11
Abstract
The simulation tools are the foundation for the design of robot systems, for the application of robots in complex environments and for the development of new control strategies and algorithms. Because of this, the design, simulation and comparison of the performance of controllers applied to a redundant robot with five degrees of freedom (DOF) are presented in this paper. Through homogeneous transformation matrices the inverse kinematic model of the redundant robot is obtained. Six controllers are prepared to test the robot’s dynamic model: hyperbolic sine–cosine; computed torque; sliding hyperbolic mode; control with learning; and adaptive. A simulation environment is developed by means of the MatLab/Simulink software, which allows analyzing the dynamic performance of the robot and of the designed controllers. This simulation environment is used to carry out different tests of the redundant manipulator model together with each controller as they are made to follow a trajectory in space. The results, obtained through a simulation environment, are represented by comparative curves and RMS indices of the joint and Cartesian errors, and they show that the redundant manipulator model follows the test trajectory with less pronounced maximum errors using the adaptive controller than the other controllers, with more homogeneous motions of the manipulator. The largest joint and Cartesian errors generated when testing the robot model, both in terms of maximum and RMS values, occurred when the computed torque controller is used. The results with that controller are obtained by executing three iterations for learning, because with more iterations the variations were not important.