Fixed-time adaptive neural control for robot manipulators with input saturation and disturbance

Huang, Haiqi; Lu, Zhenyu; Wang, Ning; Yang, Chenguang

Abstract

A fixed-time adaptive neural network control scheme is designed for an unknown model manipulator system with input saturation and external environment disturbance, so that the system convergence time can be parameterized and not affected by the initial state of the system. The compensation control item is introduced to compensate for external disturbance. The scheme can ensure that the input torque always does not exceed the actuator saturation value and the transient and steady state performance will not significantly degrade. Furthermore, the Incremental Broad Neural Network (IBNN) is used for approximating unknown models with flexible adjustability and high computational efficiency, so it can be applied to scenarios with different control precision requirements. Simulation results verify the effectiveness of the scheme in the above aspects.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2022 27th International Conference on Automation and Computing: Smart Systems and Manufacturing, ICAC 2022
• Start Date: Sep 1, 2022
• End Date: Oct 3, 2022
• Acceptance Date: Jun 14, 2022
• Publication Date: Oct 10, 2022
• Deposit Date: Oct 25, 2022
• Publicly Available Date: Oct 11, 2024
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Book Title: 2022 27th International Conference on Automation and Computing (ICAC)
• ISBN: 978-1-6654-9808-1
• DOI: https://doi.org/10.1109/ICAC55051.2022.9911082
• Keywords: Smart Systems, Manufacturing, Neural Control, Neural networks
• Public URL: https://uwe-repository.worktribe.com/output/10104129
• Publisher URL: https://ieeexplore.ieee.org/document/9911082
• Related Public URLs: http://www.cacsuk.co.uk/index.php/icac2022; ; https://ieeexplore.ieee.org/xpl/conhome/9911058/proceeding