A comparative study of LQR and integral sliding mode control strategies for position tracking control of robotic manipulators

This paper provides a systematic comparative study of position tracking control of nonlinear robotic manipulators. The main contribution of this study is a comprehensive numerical simulation assessing position tracking performances and energy consumption of integral sliding mode control (ISMC), a li...

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Bibliographic Details
Main Authors: Norsahperi, Nor Mohd. Haziq, Danapalasingam, Kumeresan A.
Format: Article
Published: Strossmayer University of Osijek , Faculty of Electrical Engineering, Computer Science and Information Technology 2019
Subjects:
Online Access:http://eprints.utm.my/id/eprint/86738/
http://dx.doi.org/10.32985/ijeces.10.2.3J.J.
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Summary:This paper provides a systematic comparative study of position tracking control of nonlinear robotic manipulators. The main contribution of this study is a comprehensive numerical simulation assessing position tracking performances and energy consumption of integral sliding mode control (ISMC), a linear-quadratic regulator with integral action (LQRT), and optimal integral sliding mode control (OISMC) under three conditions; namely, Case I) without the coupling effect, Case II) with the coupling effect on Link 1 only, and Case III) with the coupling effect on Link 2 only. The viability of the concept is evaluated based on three performance criteria, i.e., the step-response characteristics, position tracking error, and energy consumption of the aforementioned controllers. Based upon the simulation study, it has been found that OISMC offers performances almost similar to ISMC with more than 90% improvement of tracking performance under several cases compared to LQRT; however, energy consumption is successfully reduced by 3.6% in comparison to ISMC. Energy consumption of OISMC can be further reduced by applying optimization algorithms in tuning the weighting matrices. This paper can be considered significant as a robotic system with high tracking accuracy and low energy consumption is highly demanded to be implemented in smart factories, especially for autonomous systems.