Abstract
Stewart platform is widely used in motion simulator, parallel machine tool, vibration isolation, stabilization platform and so on. Due to the influence of disturbance and other factors, nonlinear sliding mode control is often used in the design of the Stewart controller. However, when subjected to large disturbances and model errors, the traditional sliding mode controller has serious switching, which leads to chattering. This paper introduces the RBF neural network to compensate for the uncertainties, and the corresponding control law and adaptive rate are designed. At the same time, the system’s stability is proved based on Lyapunov theory. The simulation results show that the designed controller is robust and can track the desired trajectory effectively.
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Acknowledgements
This work was supported by the International Science and Technology Cooperation Project of Guangdong Province under Grant 2022A0505050027, and the Project for high quality development of 6 marine industries of Department of Natural Resources of Guangdong Provincial (GDNRC[2023]32).
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Zeng, W., Yang, W., Wang, Y., Situ, W. (2023). Design of Adaptive Sliding Mode Controller Based on Neural Network Compensation for Stewart Platform. In: Jia, Y., Zhang, W., Fu, Y., Wang, J. (eds) Proceedings of 2023 Chinese Intelligent Systems Conference. CISC 2023. Lecture Notes in Electrical Engineering, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-99-6882-4_74
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DOI: https://doi.org/10.1007/978-981-99-6882-4_74
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