{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:34:41Z","timestamp":1760060081382,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T00:00:00Z","timestamp":1753747200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Provincial Department of Education","award":["2021KTSCX270"],"award-info":[{"award-number":["2021KTSCX270"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Intelligent driving is a key technology in the field of automotive manufacturing due to its advantages in environmental protection, energy efficiency, and economy. However, since the intelligent driving model is an uncertain multi-input multi-output dynamic system, especially in an interactive environment, it faces uncertainties such as non-uniform trial lengths, unknown nonlinear parameters, and unknown control direction. In this paper, an adaptive iterative learning control method is proposed for vehicle systems with non-uniform trial lengths and asymmetric control gain matrices. Unlike the existing research on adaptive iterative learning for non-uniform test lengths, this paper assumes that the elements of the system\u2019s control gain matrix are asymmetric. Therefore, the assumption made in traditional adaptive iterative learning methods that the control gain matrix of the system is known or real, symmetric, and positive definite (or negative definite) is relaxed. Finally, to prove the convergence of the system, a composite energy function is designed, and the effectiveness of the adaptive iterative learning method is verified using vehicle systems. This paper aims to address the challenges in intelligent driving control and decision-making caused by environmental and system uncertainties and provides a theoretical basis and technical support for intelligent driving, promoting the high-quality development of intelligent transportation.<\/jats:p>","DOI":"10.3390\/sym17081203","type":"journal-article","created":{"date-parts":[[2025,7,29]],"date-time":"2025-07-29T09:31:45Z","timestamp":1753781505000},"page":"1203","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Adaptive Learning Control for Vehicle Systems with an Asymmetric Control Gain Matrix and Non-Uniform Trial Lengths"],"prefix":"10.3390","volume":"17","author":[{"given":"Yangbo","family":"Tang","sequence":"first","affiliation":[{"name":"School of Railway Transportation, Guangzhou Railway Polytechnic, Guangzhou 511300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zetao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Railway Transportation, Guangzhou Railway Polytechnic, Guangzhou 511300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjun","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Railway Transportation, Guangzhou Railway Polytechnic, Guangzhou 511300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,29]]},"reference":[{"key":"ref_1","first-page":"4925","article-title":"Control systems for intelligent vehicles: A review","volume":"21","author":"Wang","year":"2020","journal-title":"IEEE Trans. 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