{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T21:36:35Z","timestamp":1773437795102,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T00:00:00Z","timestamp":1747094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>This paper presents a control framework for the image tracking of two-wheeled self-balancing carts, with the objective of achieving precise tracking control. Exploiting the remarkable memory capacity of the Long Short-Term Memory (LSTM) neural network for sequence signals, the framework conducts image memory judgment and memorization, aiming to enhance control accuracy. After the training phase, comprehensive simulations and real-world experiments are carried out based on the established model to verify the effectiveness and practicality of the proposed control strategy. The system utilizes the TSL1401 linear array CCD lens to detect black tapes on the ground and identify and memorize surrounding images. Through the establishment of a continuous set of training sample points, the LSTM network is trained using Python and TensorFlow. This training process optimizes the network\u2019s weights and generates weight files, which can be readily converted into machine code for physical implementation. Initially, the effectiveness of the control law is verified through simulating the symmetrical steering control of the two-wheeled cart. The simulation results demonstrate the validity of the proposed design method and its superior performance. Finally, a physical two-wheeled self-balancing cart is developed to further validate the feasibility of the framework. Experimental results confirm that this method is highly effective, demonstrating robust image tracking capabilities and optimal tracking performance.<\/jats:p>","DOI":"10.3390\/sym17050747","type":"journal-article","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T09:26:48Z","timestamp":1747128408000},"page":"747","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Image-Tracking-Driven Symmetrical Steering Control with Long Short-Term Memory for Linear Charge-Coupled-Device-Based Two-Wheeled Self-Balancing Cart"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0466-5002","authenticated-orcid":false,"given":"Yi-Jen","family":"Mon","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering, Ming-Chuan University, Guei-Shan District, Taoyuan City 333, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s40435-024-01518-0","article-title":"Trajectory tracking and stabilization of two-wheeled balancing mobile robot with hierarchical and sliding mode control","volume":"13","author":"Le","year":"2025","journal-title":"Int. J. Dynam. Control"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.1109\/TMECH.2020.3036579","article-title":"Self-Balancing Two-Wheeled Robot Featuring Intelligent End-to-End Deep Visual-Steering","volume":"26","author":"Li","year":"2021","journal-title":"IEEE ASME Trans. Mechatron."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yan, C., and Li, X. (2024). Research on Stability Control System of Two-Wheel Heavy-Load Self-Balancing Vehicles in Complex Terrain. Appl. Sci., 14.","DOI":"10.3390\/app14177682"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mon, Y.-J. (2025). Fuzzy PDC-Based LQR Sliding Neural Network Control for Two-Wheeled Self-Balancing Cart. Electronics, 14.","DOI":"10.3390\/electronics14091842"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Lee, T., Ju, D., and Lee, Y.S. (2025). Transition Control of a Double-Inverted Pendulum System Using Sim2Real Reinforcement Learning. Machines, 13.","DOI":"10.3390\/machines13030186"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mon, Y.-J. (2023). Tikhonov-Tuned Sliding Neural Network Decoupling Control for an Inverted Pendulum. Electronics, 12.","DOI":"10.3390\/electronics12214415"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sun, B., Li, W., Wang, Z., Zhu, Y., He, Q., Guan, X., Dai, G., Yuan, D., Li, A., and Cui, W. (2022). Recent Progress in Modeling and Control of Bio-Inspired Fish Robots. J. Mar. Sci. Eng., 10.","DOI":"10.3390\/jmse10060773"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2357011","DOI":"10.1142\/S0218001423570112","article-title":"Self-Balancing and Position Control of a Balancer System Using a Pattern-Based Intelligent Optimization Method","volume":"37","author":"Chaudhary","year":"2023","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104706","DOI":"10.1109\/ACCESS.2022.3210540","article-title":"Machine Learning Based Self-Balancing and Motion Control of the Underactuated Mobile Inverted Pendulum with Variable Load","volume":"10","author":"Unluturk","year":"2022","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhao, J., Li, J., and Zhou, J. (2023). Research on Two-Round Self-Balancing Robot SLAM Based on the Gmapping Algorithm. Sensors, 23.","DOI":"10.3390\/s23052489"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, Q., Zhou, F., Hang, R., and Yuan, X. (2017). Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyper-spectral Image Classification. Remote Sens., 9.","DOI":"10.3390\/rs9121330"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zarzycki, K., and \u0141awry\u0144czuk, M. (2021). LSTM and GRU Neural Networks as Models of Dynamical Processes Used in Predictive Control: A Comparison of Models Developed for Two Chemical Reactors. Sensors, 21.","DOI":"10.3390\/s21165625"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TASLP.2020.2987130","article-title":"Learning and Evaluation Methodologies for Polyphonic Music Sequence Prediction with LSTMs","volume":"28","author":"Ycart","year":"2020","journal-title":"IEEE ACM Trans. Audio Speech Lang. Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"33832","DOI":"10.1109\/ACCESS.2024.3372581","article-title":"LSTM-Based Framework for the Synthesis of Original Soundtracks","volume":"12","author":"Huo","year":"2024","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1109\/TITS.2019.2900426","article-title":"A 3D CNN-LSTM-Based Image-to-Image Foreground Segmentation","volume":"21","author":"Akilan","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Vishwas, B.V.K., and Macharla, S.R. (2025). Time Series Forecasting Using Generative AI: Leveraging AI for Precision Forecasting, Apress.","DOI":"10.1007\/979-8-8688-1276-7"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"124973","DOI":"10.1016\/j.apenergy.2024.124973","article-title":"TimeGPT in Load Forecasting: A Large Time Series Model Perspective","volume":"379","author":"Liao","year":"2025","journal-title":"Appl. Energy"},{"key":"ref_18","first-page":"8887","article-title":"A Comparative Analysis of TimeGPT and Time-LLM in Predicting ESP Maintenance Needs in the Oil and Gas Sector","volume":"186","author":"Paroha","year":"2024","journal-title":"Int. J. Comput. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1835798","DOI":"10.1155\/2022\/1835798","article-title":"Predictive Control of the Mobile Robot under the Deep Long-Short Term Memory Neural Network Model","volume":"2022","author":"Zheng","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2301","DOI":"10.1109\/TRO.2025.3548494","article-title":"Composite Whole-Body Control of Two-Wheeled Robots","volume":"41","author":"Zambella","year":"2025","journal-title":"IEEE Trans. Robot."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"22","DOI":"10.59247\/jfsc.v2i1.162","article-title":"Modeling and Optimal Control for Two-Wheeled Self-Balancing Robot","volume":"2","author":"Do","year":"2024","journal-title":"J. Fuzzy Syst. Control."},{"key":"ref_22","first-page":"4873425","article-title":"A Feedback-Assisted Inverse Neural Network Controller for Cart-Mounted Inverted Pen-dulum","volume":"2025","author":"Daksh","year":"2025","journal-title":"Appl. Comput. Intell. Soft Comput."},{"key":"ref_23","unstructured":"Anaconda Inc. (2025). Python Reference Manunal, Anaconda Inc.. Available online: https:\/\/anaconda.com."},{"key":"ref_24","unstructured":"Google Brain Teams (2025). Tensorflow Lite Reference Manunal, Google Brain Teams. Available online: https:\/\/www.tensorflow.org\/lite\/microcontrollers?hl=zh-tw."},{"key":"ref_25","unstructured":"Keil Electronics Co., Ltd. (2025). uVision 5 Reference Manunal, Keil Electronics Co., Ltd.. Available online: https:\/\/keil.com\/."},{"key":"ref_26","unstructured":"WheelTech Inc. (2025). Two-Wheeled Self Balancing Cart Technical Development Manual, WheelTech Inc.. Available online: https:\/\/www.wheeltec.net."},{"key":"ref_27","unstructured":"STMicroelectronics (2025). STM32F103xx Reference Manual, STMicroelectronics. Available online: https:\/\/www.st.com.cn\/content\/st_com\/zh.html."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/5\/747\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:31:51Z","timestamp":1760031111000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/5\/747"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,13]]},"references-count":27,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["sym17050747"],"URL":"https:\/\/doi.org\/10.3390\/sym17050747","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,13]]}}}