Adaptive neural network fixed‐time control for an uncertain robot with input nonlinearity

Author:

Kong Linghuan1ORCID,Ouyang Yuncheng2,Liu Zhijie3ORCID

Affiliation:

1. Department of Electrical and Computer Engineering Faculty of Science and Technology, University of Macau Macao China

2. School of Artificial Intelligence, the Engineering Research Center of Autonomous Unmanned System Technology, Ministry of Education, and Anhui Provincial Engineering Research Center for Unmanned System and Intelligent Technology Anhui University Hefei China

3. School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China

Abstract

AbstractThe article introduces an innovative adaptive fixed‐time control strategy designed for a robot system grappling with challenges like actuator saturation and model uncertainty. Two strategies are explored: model‐based control and neural networks control. In instances of model uncertainty, neural networks are leveraged to contend with the unknown dynamics of the robot system. These networks undergo training to approximate the elusive model parameters. Through this neural network approach, we establish adaptive laws grounded in fixed‐time convergence, ensuring that system tracking errors converge to a confined range near zero within a predetermined time frame. To tackle the issue of actuator saturation, an enhanced auxiliary system is introduced. This auxiliary system is tailored to counterbalance the adverse impacts of actuator saturation, thereby augmenting the tracking performance of the robot system. The proposed control policy is rigorously analyzed using Lyapunov theory, demonstrating that the system's tracking errors converge within a fixed time frame. To validate the efficacy of the proposed methodology, both numerical simulations and practical experiments are conducted, affirming the effectiveness of the approach.

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Mechanical Engineering,Aerospace Engineering,Biomedical Engineering,General Chemical Engineering,Control and Systems Engineering

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