{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:24:35Z","timestamp":1787851475273,"version":"build-2784847793"},"reference-count":15,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T00:00:00Z","timestamp":1787788800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Trajectory tracking remains one of the main challenges in mobile robotics, particularly when robots operate under real-world disturbances and modeling uncertainties. Model Predictive Control (MPC) has become one of the most effective solutions for this problem because of its ability to optimize future control actions while explicitly handling system constraints. However, despite the variety of predictive models reported in the literature, there is still limited experimental evidence regarding how predictive-model selection influences the closed-loop behavior of mobile robots. This work presents an experimental comparison between two MPC implementations: a Kinematic MPC and a multiple-input multiple-output MPC (MIMO-MPC) based on an experimentally identified state-space model. Both controllers were implemented on the same four-wheel differential-drive mobile robot running Robot Operating System 2 (ROS 2) and were configured with identical prediction horizons, weighting matrices, constraints, reference trajectories, and disturbance sequences, enabling a direct experimental comparison of the influence of predictive-model selection on closed-loop performance. Performance was assessed through trajectory-tracking accuracy, disturbance recovery, and control smoothness metrics under both nominal and externally perturbed operating conditions. The experimental evaluation was performed on real hardware at 50 Hz using a closed-loop trajectory of approximately 20 m. Under nominal conditions, the kinematic MPC achieved a lower lateral root mean square error (RMSE) (0.1031 m versus 0.1187 m) and maintained 93.23% of the trajectory within the \u00b10.20 m tolerance band. Under external perturbations, however, the MIMO-MPC reduced the heading RMSE from 0.7617 rad to 0.6503 rad, shortened the recovery time after the two largest disturbances by up to 35.6%, and decreased the angular-velocity rate root mean square (RateRMS) and jerk root mean square (JerkRMS) by approximately 24.7% and 24.8%, respectively. These results demonstrate that predictive-model selection has a significant influence on the transient behavior of MPC. While a conventional kinematic model provides excellent nominal tracking performance, an experimentally identified MIMO predictive model offers faster recovery and smoother control under disturbed conditions. The proposed experimental methodology provides practical evidence that can assist the selection of predictive models for MPC-based mobile robots according to the expected operating conditions.<\/jats:p>","DOI":"10.3390\/s26175431","type":"journal-article","created":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:47:13Z","timestamp":1787849233000},"page":"5431","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative Analysis of Kinematic and Identified MIMO Models in Model Predictive Control for Mobile Robot Trajectory Tracking"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1244-291X","authenticated-orcid":false,"given":"Diego","family":"Guffanti","sequence":"first","affiliation":[{"name":"Facultad de Ciencias de la Ingenier\u00eda e Industrias, Universidad UTE, Quito 170129, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9319-8815","authenticated-orcid":false,"given":"Wilson","family":"Pavon","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias de la Ingenier\u00eda e Industrias, Universidad UTE, Quito 170129, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6397-6463","authenticated-orcid":false,"given":"Alfredo","family":"Zapata","sequence":"additional","affiliation":[{"name":"Departamento de Ciencias Exactas, Universidad de las Fuerzas Armadas ESPE, Sangolqu\u00ed 171103, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1016\/S0005-1098(99)00214-9","article-title":"Constrained Model Predictive Control: Stability and Optimality","volume":"36","author":"Mayne","year":"2000","journal-title":"Automatica"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xu, C., Zhou, X., Chen, R., Li, B., He, W., Li, Y., and Ye, F. (2025). Trajectory Tracking for 3-Wheeled Independent Drive and Steering Mobile Robot Based on Dynamic Model Predictive Control. Appl. Sci., 15.","DOI":"10.3390\/app15010485"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2693","DOI":"10.1049\/cth2.12709","article-title":"A Linear MPC with Control Barrier Functions for Differential Drive Robots","volume":"18","author":"Ali","year":"2024","journal-title":"IET Control Theory Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1177\/00202940221092134","article-title":"Trajectory tracking of differential drive mobile robots using fractional-order proportional-integral-derivative controller design tuned by an enhanced fruit fly optimization","volume":"55","author":"Abed","year":"2022","journal-title":"Meas. Control"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, K., Wang, J., Xin, X., Li, X., Sun, C., Huang, J., and Kong, W. (2022). A Survey on Learning-Based Model Predictive Control: Toward Path Tracking Control of Mobile Platforms. Appl. Sci., 12.","DOI":"10.3390\/app12041995"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"105152","DOI":"10.1016\/j.robot.2025.105152","article-title":"Trajectory Tracking Model Predictive Control for Mobile Robot Based on Deep Koopman Operator Modeling","volume":"194","author":"Tang","year":"2025","journal-title":"Robot. Auton. Syst."},{"key":"ref_7","first-page":"20","article-title":"Model Predictive Control of a Differential-Drive Mobile Robot","volume":"10","author":"Bouzoualegh","year":"2018","journal-title":"Acta Univ. Sapientiae Electr. Mech. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Mondal, K., Rodriguez, A.A., Manne, S.S., Das, N., and Wallace, B. (2019, January 10\u201312). Comparison of Kinematic and Dynamic Model Based Linear Model Predictive Control of Non-Holonomic Robot for Trajectory Tracking: Critical Trade-offs Addressed. Proceedings of the American Control Conference, Philadelphia, PA, USA.","DOI":"10.2316\/P.2019.860-017"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1049\/iet-cta.2017.0395","article-title":"Trajectory Tracking for Wheeled Mobile Robots via Model Predictive Control with Softening Constraints","volume":"12","author":"Yang","year":"2018","journal-title":"IET Control Theory Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1109\/TCST.2007.894653","article-title":"Predictive Active Steering Control for Autonomous Vehicle Systems","volume":"15","author":"Falcone","year":"2007","journal-title":"IEEE Trans. Control Syst. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2576","DOI":"10.1109\/TMECH.2017.2758603","article-title":"Disturbance Rejection MPC for Tracking of Wheeled Mobile Robot","volume":"22","author":"Sun","year":"2017","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.automatica.2017.12.048","article-title":"Robust MPC for Tracking Constrained Unicycle Robots with Additive Disturbances","volume":"90","author":"Sun","year":"2018","journal-title":"Automatica"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6669","DOI":"10.1016\/j.jfranklin.2023.05.004","article-title":"Model Predictive Tracking Control with Disturbance Compensation for Wheeled Mobile Robots in an Environment with Obstacles","volume":"360","author":"Li","year":"2023","journal-title":"J. Frankl. Inst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.isatra.2024.05.050","article-title":"Sliding Mode Observer-Based Model Predictive Tracking Control for Mecanum-Wheeled Mobile Robot","volume":"151","author":"Wang","year":"2024","journal-title":"ISA Trans."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guffanti, D., Mora Murillo, M.F., Bustamante Sanchez, S., Obreg\u00f3n Guti\u00e9rrez, J.O., Hinojosa, M.A., Brunete, A., Hernando, M., and \u00c1lvarez, D. (2026). Robust Model Predictive Control with a Dynamic Look-Ahead Re-Entry Strategy for Trajectory Tracking of Differential-Drive Robots. Sensors, 26.","DOI":"10.3390\/s26020520"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5431\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T16:47:46Z","timestamp":1787849266000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5431"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,27]]},"references-count":15,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175431"],"URL":"https:\/\/doi.org\/10.3390\/s26175431","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,27]]}}}