Robotics & Machine Learning Daily News2024,Issue(Feb.29) :85-86.DOI:10.1007/s12555-022-0357-4

New Findings from Xihua University in the Area of Robotics Reported (Adaptive Hierarchical Sliding Mode Control Based On Extended State Observer for Underactuated Robotic System)

Robotics & Machine Learning Daily News2024,Issue(Feb.29) :85-86.DOI:10.1007/s12555-022-0357-4

New Findings from Xihua University in the Area of Robotics Reported (Adaptive Hierarchical Sliding Mode Control Based On Extended State Observer for Underactuated Robotic System)

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Abstract

A new study on Robotics is now available. According to news originating from Chengdu, People's Republic of China, by NewsRx correspondents, research stated, "In order to stabilize underactuated robotic systems with external disturbances, an adaptive hierarchical sliding mode control strategy based on extended state observer is proposed. The extended state observer is designed to estimate the joint states and lumped disturbance composed of matched and unmatched disturbances." Financial supporters for this research include Natural Science Foundation of Sichuan, National Natural Science Foundation of China (NSFC). Our news journalists obtained a quote from the research from Xihua University, "The underactuated robotic system is divided into two subsystems. For each subsystem, a sub-sliding mode surface is constructed to obtain the first layer sliding mode surface and the second layer sliding mode surface is derived from the first layer sliding mode surface. Then the hierarchical sliding mode controller is designed with the estimated state obtained from the observer to compensate the lumped disturbance and an adaptive law is designed to adjust the switching gain. The stability of the system is proved by Lyapunov theory and the effectiveness of the proposed control strategy is verified by comparative simulations."

Key words

Chengdu/People's Republic of China/Asia/Emerging Technologies/Machine Learning/Robotics/Robots/Xihua University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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