Dynamic Neural-Model-Based Predictive Control for Autonomous Wheel-Legged Robot System

Abstract

Mobile wheel-legged robots exhibiting mobility, stability and reliability have garnered heightened research attention in demanding real-world scenarios, especially in material transport, emergency response and space exploration. The kinematics model merely delineates the geometric relationship of the controlled objective, disregarding force feedback. This study in-vestigates model predictive trajectory tracking control utilising the robot dynamic model (DRMPC) in the context of unpre-dictable interactions. The predictive tracking controller for the wheel-legged robot is introduced in the context of position tracking. A dynamic approximator is employed to address the uncertain interactions in the tracking process. Ultimately, co- simulation and empirical tests are conducted to demonstrate the efficacy of the devised control methodology, which ach-ieves high precision and dependable robustness. This work can elucidate the technical and practical oversight of autonomous movement in complicated environments and enhance the manoeuverability and fiexibility.

Keywords

intelligent control / predictive control / robotics

Acknowledgements

This work was supported by the National Natural Science Foundation of China (62203176, 62173038), Guangzhou Key Research and Develop-ment Program (2025B03J0072), Guangdong High-Level Talents Special Support Programme (2024TQ08Z107), Anhui Province Natural Science Funds for Distinguished Young Scholar (2308085J02), State Key Labo-ratory of Intelligent Vehicle Safety Technology (IVSTSKL-202402, IVSTSKL-202430, IVSTSKL-202508, IVSTSKL-202520), State Key Labo-ratory of Intelligent Green Vehicle and Mobility (KFY2417), State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body (32215010), and Wuhu Major Scientific and Technological Achieve-ments Engineering Project (2021zc04).

Conflicts of Interest

The authors declare no confiicts of interest.

Data Availability Statement

The data presented in this study are available on request from the first author.

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