Adaptive control of a two-link robot using batch least-square identifier

• Published in: IEEE/CAA journal of automatica sinica Vol. 8; no. 1; pp. 86 - 93
• Main Authors: Bagheri, Mostafa, Karafyllis, Iasson, Naseradinmousavi, Peiman, Krstic, Miroslav
• Format: Journal Article
• Language: English
• Published: Piscataway Chinese Association of Automation (CAA) 01.01.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Department of Mechanical and Aerospace Engineering, University of California San Diego and San Diego State University, La Jolla, CA 92037 USA%Department of Mathematics, National Technical University of Athens, Athina 10682, Greece%Department of Mechanical Engineering, San Diego, CA 92182 USA%Department of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, CA 92037 USA
• Subjects: Adaptive control; Algorithms; Asymptotic properties; Control algorithms; Control systems design; Control theory; Convergence; Manipulator dynamics; Manipulators; Mathematical model; Nonlinear systems; Parameter estimation; Robot arms; Robot control; Robots; Stability; Symmetric matrices; Uncertainty; robot manipu-lators; least-square identifier; Backstepping; trigger-based adaptive control
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2020.1003459

We design a regulation-triggered adaptive controller for robot manipulators to efficiently estimate unknown parameters and to achieve asymptotic stability in the presence of coupled uncertainties. Robot manipulators are widely used in telemanipulation systems where they are subject to model and environmental uncertainties. Using conventional control algorithms on such systems can cause not only poor control performance, but also expensive computational costs and catastrophic instabilities. Therefore, system uncertainties need to be estimated through designing a computationally efficient adaptive control law. We focus on robot manipulators as an example of a highly nonlinear system. As a case study, a 2-DOF manipulator subject to four parametric uncertainties is investigated. First, the dynamic equations of the manipulator are derived, and the corresponding regressor matrix is constructed for the unknown parameters. For a general nonlinear system, a theorem is presented to guarantee the asymptotic stability of the system and the convergence of parameters&#x02BC estimations. Finally, simulation results are discussed for a two-link manipulator, and the performance of the proposed scheme is thoroughly evaluated.