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The kinematics model merely delineates the geometric relationship of the controlled objective, disregarding force feedback. This study investigates model predictive trajectory tracking control utilising the robot dynamic model (DRMPC) in the context of unpredictable interactions. The predictive tracking controller for the wheel\u2010legged 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\u2010simulation and empirical tests are conducted to demonstrate the efficacy of the devised control methodology, which achieves 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 flexibility.<\/jats:p>","DOI":"10.1049\/cit2.70091","type":"journal-article","created":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T12:19:58Z","timestamp":1764677998000},"page":"83-97","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Neural\u2010Model\u2010Based Predictive Control for Autonomous Wheel\u2010Legged Robot System"],"prefix":"10.1049","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4946-4434","authenticated-orcid":false,"given":"Jiehao","family":"Li","sequence":"first","affiliation":[{"name":"National Key Laboratory of Agricultural Equipment Technology South China Agricultural University  Guangzhou China"},{"name":"School of Automation Beijing Institute of Technology  Beijing China"},{"name":"School of Computer Science and Engineering South China University of Technology  Guangzhou China"},{"name":"State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing Changan Automobile Company Ltd.  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