Neural Adaptive Control for Robotic Systems With Saturation and Disturbance

This paper proposes a neural adaptive control (NAC) strategy for the robotic systems with saturation and disturbance. The strategy involves using a neural network (NN) to estimate the robot's dynamic model and constructing a compensator to mitigate the impact of input saturation. To obtain esti...

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Bibliographic Details
Published in2023 8th International Conference on Automation, Control and Robotics Engineering (CACRE) pp. 242 - 247
Main Authors Ding, Shuai, Peng, Jinzhu, Liu, Yan, Wu, Yage
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2023
Subjects
adaptive control
Artificial neural networks
Automation
Error compensation
extended state observer
input saturation
Manipulators
neural network
Observers
robotic systems
Stability analysis
Torque
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Summary:This paper proposes a neural adaptive control (NAC) strategy for the robotic systems with saturation and disturbance. The strategy involves using a neural network (NN) to estimate the robot's dynamic model and constructing a compensator to mitigate the impact of input saturation. To obtain estimated velocities and external disturbances in the robotic systems without prior knowledge of the system model, an NN-based extended state observer (NNBESO) is designed. Based on the designed error compensation term and NNBESO, NAC is proposed to achieve the tracking for the desired trajectory under input saturation and external disturbance. Finally, the proposed NNBESO-based NAC strategy is verified by the simulation experiments on a 2 rigid-link robotic manipulator with input saturation and system disturbances, and the results demonstrate the effectiveness of the proposed NNBESO-based NAC strategy.
DOI:10.1109/CACRE58689.2023.10208442