An Artificial Neural Networks (ANN) Approach for 3 Degrees of Freedom Motion Controlling

Truong Cong My - School of Electrical and Electronics Engineering, Vietnam Maritime University, Viet Nam
Le Dang Khanh - Marine Engineering Department, Vietnam Maritime University, Viet Nam
Pham Minh Thao - School of Electrical and Electronics Engineering, Vietnam Maritime University, Viet Nam


Citation Format:



DOI: http://dx.doi.org/10.30630/joiv.7.2.1817

Abstract


Maritime simulation systems provide opportunities to acquire technical, procedural, and operational skills without the risks and expenses associated with on-the-job training. Maritime simulation systems are tools used to simulate real-world scenarios for training and research purposes, in which they are used to train seafarers in a safe and controlled environment. These systems are used to simulate different scenarios, such as navigation, maneuvering, and ship handling. The simulation systems allow users to learn and practice different scenarios without exposing themselves to real-life risks. However, at the moment, Vietnam's maritime simulators are dependent on other nations, which results in a lack of technological autonomy, a lengthy transfer of technology, high expenses, and a reduction in national security. Therefore, there is a lot of interest in developing a domestic maritime simulation system. With a rotation angle of α = [α1 α2 α3]T from the PLC controlling the DC/Servo system, the motion platform of the marine simulation system is built on the Stewart platform design principle. Due to the use of conventional control methods, this system suffers from a time delay of up to 1200ms, which prevents it from reacting to real-time control. In this paper, we investigate a novel technique for controlling the dynamic model with three degrees of freedom (3 DOF) of a cockpit cabin deck using artificial neural networks. The findings demonstrate that the reaction to real-time control, rotation error, and drive/servo system movement are all greatly improved.

Keywords


Maritime simulation system; cockpit cabin deck; neural networks; AI algorithm; multi-layer perceptron; time delay

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References


D. Reiher and A. Hahn, “Review on the Current State of Scenario- and Simulation-Based V&V in Application for Maritime Traffic Systems,†in OCEANS 2021: San Diego – Porto, Sep. 2021, pp. 1–9, doi: 10.23919/OCEANS44145.2021.9705781.

Z. Liang, X. Qu, Z. Zhang, and C. Chen, “Three-Dimensional Path-Following Control of an Autonomous Underwater Vehicle Based on Deep Reinforcement Learning,†Polish Marit. Res., vol. 29, no. 4, pp. 36–44, Dec. 2022, doi: 10.2478/pomr-2022-0042.

G. Yan, Y. Hu, and Q. Shi, “A Convolutional Neural Network-Based Method of Inverter Fault Diagnosis in a Ship’s DC Electrical System,†Polish Marit. Res., vol. 29, no. 4, pp. 105–114, Dec. 2022, doi: 10.2478/pomr-2022-0048.

Y. Shi, J. Du, C. R. Ahn, and E. Ragan, “Impact assessment of reinforced learning methods on construction workers’ fall risk behavior using virtual reality,†Autom. Constr., vol. 104, pp. 197–214, Aug. 2019, doi: 10.1016/j.autcon.2019.04.015.

Z. Świder, L. Trybus, and A. Stec, “Consistent Design of PID Controllers for an Autopilot,†Polish Marit. Res., vol. 30, no. 1, pp. 78–85, Mar. 2023, doi: 10.2478/pomr-2023-0008.

S. C. Mallam, S. Nazir, and S. K. Renganayagalu, “Rethinking Maritime Education, Training, and Operations in the Digital Era: Applications for Emerging Immersive Technologies,†J. Mar. Sci. Eng., vol. 7, no. 12, p. 428, Nov. 2019, doi: 10.3390/jmse7120428.

A. Azis, D. Krisbiantoro, and R. -, “Internet of Things (IoT) Innovation and Application to Intelligent Governance Systems: A Case Study on DISHUB for Transport Vehicles,†JOIV Int. J. Informatics Vis., vol. 7, no. 1, pp. 193–199, Feb. 2023, doi: 10.30630/joiv.7.1.1282.

X. Li and D. Zhu, “An Adaptive SOM Neural Network Method to Distributed Formation Control of a Group of AUVs,†IEEE Trans. Ind. Electron., pp. 1–1, 2018, doi: 10.1109/TIE.2018.2807368.

X. K. Dang, H. D. Tran, and D. C. Quach, “Ship Autopilot Design Based on Adaptive Smith Predictor Under the Effect of Uncertain Time-delay and Disturbances,†2012.

X.-K. Dang, Z.-H. Guan, H.-D. Tran, and T. Li, “Fuzzy adaptive control of networked control system with unknown time-delay,†in Proceedings of the 30th Chinese Control Conference, 2011, pp. 4622–4626.

A. W. Abdul Ali, F. A. Abdul Razak, and N. Hayima, “A Review on The AC Servo Motor Control Systems,†Elektr. J. Electr. Eng., vol. 19, no. 2, pp. 22–39, Aug. 2020, doi: 10.11113/elektrika.v19n2.214.

B. Erfianto and A. Rahmatsyah, “Application of ARIMA Kalman Filter with Multi-Sensor Data Fusion Fuzzy Logic to Improve Indoor Air Quality Index Estimation,†JOIV Int. J. Informatics Vis., vol. 6, no. 4, pp. 771–777, Dec. 2022, doi: 10.30630/joiv.6.4.889.

K. J. Åström and C. G. Källström, “Identification of ship steering dynamics,†Automatica, vol. 12, no. 1, pp. 9–22, Jan. 1976, doi: 10.1016/0005-1098(76)90064-9.

D. Borkin, A. Nemethova, M. Nemeth, and P. Tanuska, “Control of a Production Manipulator with the Use of BCI in Conjunction with an Industrial PLC,†Sensors, vol. 23, no. 7, p. 3546, Mar. 2023, doi: 10.3390/s23073546.

I. González, A. Calderón, A. Mejías, and J. Andújar, “Novel Networked Remote Laboratory Architecture for Open Connectivity Based on PLC-OPC-LabVIEW-EJS Integration. Application in Remote Fuzzy Control and Sensors Data Acquisition,†Sensors, vol. 16, no. 11, p. 1822, Oct. 2016, doi: 10.3390/s16111822.

S. Kang, M. G. Lee, and Y.-M. Choi, “Six Degrees-of-Freedom Direct-Driven Nanopositioning Stage Using Crab-Leg Flexures,†IEEE/ASME Trans. Mechatronics, vol. 25, no. 2, pp. 513–525, Apr. 2020, doi: 10.1109/TMECH.2020.2972301.

R. Suzuki, M. Ueno, and Y. Tsukada, “Numerical simulation of 6-degrees-of-freedom motions for a manoeuvring ship in regular waves,†Appl. Ocean Res., vol. 113, p. 102732, Aug. 2021, doi: 10.1016/j.apor.2021.102732.

J. Velagic, Z. Vukic, and E. Omerdic, “Adaptive fuzzy ship autopilot for track-keeping,†Control Eng. Pract., vol. 11, no. 4, pp. 433–443, Apr. 2003, doi: 10.1016/S0967-0661(02)00009-6.

M. Nahiduzzaman, M. J. Nayeem, M. T. Ahmed, and M. S. U. Zaman, “Prediction of Heart Disease Using Multi-Layer Perceptron Neural Network and Support Vector Machine,†in 2019 4th International Conference on Electrical Information and Communication Technology (EICT), Dec. 2019, pp. 1–6, doi: 10.1109/EICT48899.2019.9068755.

E. A. Zanaty, “Support Vector Machines (SVMs) versus Multilayer Perception (MLP) in data classification,†Egypt. Informatics J., vol. 13, no. 3, pp. 177–183, Nov. 2012, doi: 10.1016/j.eij.2012.08.002.

N. F. Muhamad Krishnan, Z. A. Zukarnain, A. Ahmad, and M. Jamaludin, “Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models,†JOIV Int. J. Informatics Vis., vol. 6, no. 3, pp. 597–603, Sep. 2022, doi: 10.30630/joiv.6.2.788.

A. E. Minarno, T. D. Antoko, and Y. Azhar, “Generation of Batik Patterns Using Generative Adversarial Network with Content Loss Weighting,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 1, pp. 348–356, Jan. 2023, doi: 10.18517/ijaseit.13.1.16201.

N. Nurhamidah, A. Junaidi, and A. H. Yogyantoro, “Performance Evaluation of the Urban Drainage Network Structure Using the SWMM Model,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 2, pp. 462–468, Apr. 2023, doi: 10.18517/ijaseit.13.2.16986.

J. Naskath, G. Sivakamasundari, and A. A. S. Begum, “A Study on Different Deep Learning Algorithms Used in Deep Neural Nets: MLP SOM and DBN,†Wirel. Pers. Commun., vol. 128, no. 4, pp. 2913–2936, Feb. 2023, doi: 10.1007/s11277-022-10079-4.

S. Shisode and G. Shrigandhi, “Design and Simulation of 3Degree of Freedom ( DOF ) Motion Platform,†vol. 7, no. 7, pp. 38–44, 2017.

S. Goyal and P. K. Bhatia, “A Non-Linear Technique for Effective Software Effort Estimation using Multi-Layer Perceptrons,†in 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Feb. 2019, pp. 1–4, doi: 10.1109/COMITCon.2019.8862256.

U. G. Inyang, F. F. Ijebu, F. B. Osang, A. A. Afoluronsho, S. S. Udoh, and I. J. Eyoh, “A Dataset-Driven Parameter Tuning Approach for Enhanced K-Nearest Neighbour Algorithm Performance,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 1, pp. 380–391, Jan. 2023, doi: 10.18517/ijaseit.13.1.16706.

S. N. Wahyuni, N. N. Khanom, and Y. Astuti, “K-Means Algorithm Analysis for Election Cluster Prediction,†JOIV Int. J. Informatics Vis., vol. 7, no. 1, pp. 1–6, Jan. 2023, doi: 10.30630/joiv.7.1.1107.

L. Moreira and C. G. Soares, “Neural network model for estimation of hull bending moment and shear force of ships in waves,†Ocean Eng., vol. 206, p. 107347, Jun. 2020, doi: 10.1016/j.oceaneng.2020.107347.

F. Z. Lhafra and O. Abdoun, “Integration of Adaptive Collaborative Learning Process in a Hybrid Learning Environment,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 2, pp. 638–650, Apr. 2023, doi: 10.18517/ijaseit.13.2.16608.

A. Rahman, S. Wahjuni, and K. Priandana, “The Development of Hydroponic Nutrient Solutions Control Using Fuzzy and BPNN for Celery Plant,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 1, pp. 431–436, Jan. 2022, doi: 10.18517/ijaseit.12.1.13833.

S. J. Prashantha and H. N. Prakash, “Two-Stage Approach of Hierarchical Deep Feature Representation and Fusion Frameworks for Brain Image Analysis,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 4, pp. 1372–1378, Jul. 2022, doi: 10.18517/ijaseit.12.4.16006.

N. F. Rozam and M. Riasetiawan, “XGBoost Classifier for DDOS Attack Detection in Software Defined Network Using sFlow Protocol,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 2, pp. 718–725, Apr. 2023, doi: 10.18517/ijaseit.13.2.17810.

R. Suneth, H. Sukoco, and S. N. Neyman, “Botnet Detection Model in Encrypted Traffics Software-Defined Network (SDN) Using Deep Neural Network (DNN),†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 13, no. 2, pp. 744–750, Apr. 2023, doi: 10.18517/ijaseit.13.2.9370.

A. B. W. Putra, A. F. O. Gaffar, M. T. Sumadi, and L. Setiawati, “Intra-frame Based Video Compression Using Deep Convolutional Neural Network (DCNN),†JOIV Int. J. Informatics Vis., vol. 6, no. 3, pp. 650–659, Sep. 2022, doi: 10.30630/joiv.6.3.1012.

P. M. Afgatiani et al., “Assessing LAPAN-A3 Satellite with Line Imager Space Application (LISA) Sensor for Oil Spill Detection,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 6, pp. 2165–2173, Nov. 2022, doi: 10.18517/ijaseit.12.6.16076.

R. Jia, “Design of a simulation platform for intelligent marine search and rescue based on wireless sensors,†Microprocess. Microsyst., vol. 80, p. 103572, Feb. 2021, doi: 10.1016/j.micpro.2020.103572.

K. Liang et al., “Robust adaptive neural networks control for dynamic positioning of ships with unknown saturation and time-delay,†Appl. Ocean Res., vol. 110, p. 102609, May 2021, doi: 10.1016/j.apor.2021.102609.