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Due to this, designing an underwater trajectory tracking system based on a kinematic model is a challenging task. In order to achieve the high-precision trajectory tracking control of underwater tracked vehicles, this paper proposes a trajectory tracking algorithm based on the kinematic model of tracked vehicles. First, an inverse tangent algorithm is proposed to address the problem that the front viewpoint of the Stanley algorithm may not be on the target path and cause deviation. Second, by using the inverse tangent algorithm to introduce the target bow angle, as well as combining the mechanisms by which biological endocrine hormones are regulated with the advantages of single-neuron proportional\u2013integral\u2013derivative (PID), a composite endocrine intelligent control law is proposed for the design of the lateral control law of an underwater tracked vehicle; a longitudinal control law is designed based on the longitudinal offset of the ICR obtained by the estimation of the unscented Kalman filter as a means of compensating for longitudinal errors. Finally, a kinematic model is used in order to determine the desired drive wheel speed of the underwater tracked vehicle. The method is compared with the method without longitudinal deviation compensation, then with the methods presented in related literature, and finally, a certain degree of experimental validation is carried out. These results validate the effectiveness of the proposed method.<\/jats:p>","DOI":"10.1177\/01423312241279487","type":"journal-article","created":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T13:27:26Z","timestamp":1728221246000},"page":"2076-2088","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Trajectory tracking control of underwater tracked vehicle considering ICR longitudinal deviation compensation"],"prefix":"10.1177","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4405-6192","authenticated-orcid":false,"given":"Nan","family":"Li","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenjie","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,10,6]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s42835-020-00453-2"},{"issue":"9","key":"e_1_3_3_3_1","first-page":"47","article-title":"Real-time path tracking of mobile robot based on nonlinear model predictive control","volume":"51","author":"Bai G","year":"2020","unstructured":"Bai G, Liu L, Meng Y, et al. 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