Abstract
This paper presents a free-model high-precision controller for robot manipulators using variation sliding mode scheme and a semi-positive neural-network design. The total dynamics of the robot is first estimated by the neural network in which a new type of learning laws is proposed based on twisting excitation signals. A flexible sliding mode control interface is then developed to realize control objectives using the estimation result of the neural network. The control performance of the closed-loop system is verified by the intensively theoretical proof and simulation results.
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The authors are very grateful to the referees and editors for their valuable comments, which helped to improve the paper quality. This work is funded by the Vietnam National Foundation for Science and Technology (NAFOSTED) under grant number 107.01-2020.10.
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Ba, D.X. (2021). A Flexible Sliding Mode Controller for Robot Manipulators Using a New Type of Neural-Network Predictor. In: Huang, YP., Wang, WJ., Quoc, H.A., Giang, L.H., Hung, NL. (eds) Computational Intelligence Methods for Green Technology and Sustainable Development. GTSD 2020. Advances in Intelligent Systems and Computing, vol 1284. Springer, Cham. https://doi.org/10.1007/978-3-030-62324-1_15
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