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Path Following Control in 6G Communication Network Based on Model Predictive Control

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Abstract

With the research and development of 6G communication network technology, the path following controller of wheeled mobile robot will be further improved and optimized with the development of 6G technology. The problem of existing system is speed input is inconsistent with the actual speed input in the method of path tracking, so to overcome this issue, this paper proposes an optimal control algorithm of inversion path tracking model based on model predictive control. Firstly, depend on the kinematic model of mobile robot in 6G communication network, the velocity control law of the backstepping tracking model is designed, as well as through Lyapunov stability theorem, the following junction of the model is proved; then, the backstepping path tracking model is optimized based on model predictive control algorithm, which enhances the tenacity of path tracking when the velocity input is inconsistent with the actual velocity. Finally, a path tracking simulation method is introduced to authenticate the optimization algorithm depends on the kinematics model of mobile robot under 6G communication network. By associated with the general inversion control algorithm, the proposed path tracking algorithm has a high improvement in tracking robustness and stability, which is more appropriate for the application of mobile robot path tracking control.

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Acknowledgements

This work was supported, the National Natural Science Foundation of China (Grant No. 61932012, 62102033), the Beijing Municipal Commission of Education Project (No.KM202111417001), the Collaborative Innovation Center for Visual Intelligence (Grant No. CYXC2011), the Academic Research Projects of Beijing Union University (No. ZB10202003, ZK40202101, ZK120202104).

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Correspondence to Hong Bao.

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Li, P., Xu, C. & Bao, H. Path Following Control in 6G Communication Network Based on Model Predictive Control. Int J Wireless Inf Networks 30, 75–85 (2023). https://doi.org/10.1007/s10776-021-00533-8

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  • DOI: https://doi.org/10.1007/s10776-021-00533-8

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