Xie, Y, Tang, X, Meng, W orcid.org/0000-0003-0209-8753 et al. (4 more authors) (2019) Iterative Data-Driven Fractional Model Reference Control of Industrial Robot for Repetitive Precise Speed Tracking. IEEE/ASME Transactions on Mechatronics, 24 (3). pp. 1041-1053. ISSN 1083-4435
Abstract
Nowadays, industrial robots have been widely used in manufacturing applications; however, their speed is hard to control precisely due to unknown system dynamics. Instead of struggling to get an accurate explicit model, this paper addresses this challenge by proposing a new iterative data-driven fractional model reference control (FMRC) method, which tunes an adaptive controller to ensure that each robot actuation system behaves closely to a reference model with desirable system behavior to be obtained. This method utilizes input-output measurements without requiring an identified model or accessing the plant through specific experiments. A multiple degrees-of-freedom FMRC method with self-learning ability is designed to iteratively reach the optimal control parameters such that an accurate speed tracking is attained for each actuator. Constraints on the input signal are also considered to enhance the system robustness against external disturbances. The convergence, asymptotic accuracy, and stability of the designed control system are analyzed theoretically. Experimental results indicate that the proposed FMRC method is able to achieve a higher tracking precision and better robustness for the industrial robot compared with conventional methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Keywords: | Data-driven; fractional model reference control (FMRC); industrial robot; speed tracking |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds) |
Funding Information: | Funder Grant number EPSRC EP/S019219/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Jul 2019 10:48 |
Last Modified: | 23 Jul 2019 08:52 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/TMECH.2019.2906643 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:148768 |