Hostname: page-component-cb9f654ff-65tv2 Total loading time: 0 Render date: 2025-08-08T11:01:24.481Z Has data issue: false hasContentIssue false
  • English
  • Français

Data-driven controller adaptation and parameter estimation in autonomous underwater vehicles by genetic algorithm enhanced unfalsification approach

Published online by Cambridge University Press:  06 August 2025

Tabassum Rasul Open the ORCID record for Tabassum Rasul [Opens in a new window]  and
Koena Mukherjee Open the ORCID record for Koena Mukherjee [Opens in a new window]
Tabassum Rasul*
Affiliation:
NIT Silchar, Cachar, Assam, India
Koena Mukherjee
Affiliation:
NIT Silchar, Cachar, Assam, India
*
Corresponding author: Tabassum Rasul; Email: tabassum_rs@ei.nits.ac.in
Get access Rights & Permissions [Opens in a new window]

Abstract

The paper presents an enhanced method for unknown parameter estimation and nonlinear controller adaptation that combines the concept of unfalsification with the genetic algorithm (GA). This approach is based on the measured data and employs a bank of nonlinear controllers designed to dynamically adjust to the system’s evolving conditions. The controllers in the bank can be switched to meet the system’s requirements. This method is applied to an autonomous underwater vehicle (AUV) with uncertain parameters. Using the unfalsification method, these uncertain parameters are estimated, and a suitable controller is selected from the bank to guide the AUV along a desired trajectory. Additionally, an artificial intelligence technique, such as the GA, is employed to update the controller bank, resulting in versatile and optimised candidates. The simulation results obtained in the MATLAB/Simulink environment show that in the environment considered in this paper, the Adaptive Unfalsification algorithm in conjunction with GA estimates the unknown parameter values better than the sole GA-optimised values. Also, the convergence of the actual trajectory of the AUV using the Adaptive Unfalsification algorithm in conjunction with GA is faster and better than the sole GA-optimised algorithm. Furthermore, a survey of experimental results from established literature is included to evaluate the practical implementation of the proposed design, which concludes that within a reasonable time, the Adaptive Unfalsification algorithm in conjunction with GA can be implemented in commercially available processors.

Keywords

data-driven controladaptive controlunfalsified controlgenetic algorithmautonomous underwater vehicle

Information

Type
Research Article
Information
Robotica , First View , pp. 1 - 28
Copyright
© The Author(s), 2025. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Jun, M. and Safonov, M. G., “Automatic Pid Tuning: An Application of Unfalsified Control,” In: Proceedings of the International Symposium on Computer Aided Control System Design (1999).Google Scholar
Williams, S. B., Newman, P., Rosenblatt, J., Dissanayake, G. and Durrant-Whyte, H., “Autonomous underwater navigation and control,” Robotica 19(5), 481–496 (2001). doi: 10.1017/S0263574701003423.CrossRefGoogle Scholar
Hosseini, M., Ranjbar Noei, A. and Rostami, S. J. S., “Trajectory tracking control of an underwater vehicle in the presence of disturbance, measurement errors, and actuator dynamic and nonlinearity,” Robotica 41(10), 3059–3078 (2023). doi: 10.1017/S0263574723000875.CrossRefGoogle Scholar
Tan, K. M., Anvar, A. and Lu, T., “Autonomous underwater vehicle (auv) dynamics modelling and performance evaluation,” Int. J. Mech. Mechatron. Eng. 6(10), (2012).Google Scholar
Izadbakhsh, A. and Khorashadizadeh, S., “Polynomial-based robust adaptive impedance control of electrically driven robots,” Robotica 39(7), 1181–1201 (2021). doi: 10.1017/S0263574720001009.CrossRefGoogle Scholar
Sahu, B. K. and Subudhi, B., “Adaptive tracking control of an autonomous underwater vehicle,” Int. J. Autom. Comput. 11(3), 299–307 (2014).10.1007/s11633-014-0792-7CrossRefGoogle Scholar
Izadbakhsh, A., Khorashadizadeh, S. and Kheirkhahan, P., “Tracking control of electrically driven robots using a model-free observer,” Robotica 37(4), 729–755 (2019). doi: 10.1017/S0263574718001303.CrossRefGoogle Scholar
Ismail, Z. H. and Dunnigan, M. W., “Nonlinear $h_{\infty }$ optimal control scheme for an underwater vehicle with regional function formulation,” J. Appl. Math. 2013(1), 732738 (2013).Google Scholar
Shojaei, K., “Three-dimensional tracking control of autonomous underwater vehicles with limited torque and without velocity sensors,” Robotica 36(3), 374–394 (2018). doi: 10.1017/S0263574717000455.CrossRefGoogle Scholar
Wu, C., Dai, Y., Shan, L., Zhu, Z. and Wu, Z., “Data-driven trajectory tracking control for autonomous underwater vehicle based on iterative extended state observer,” Math. Biosci. Eng.: MBE 19(3), 3036–3055 (2022).10.3934/mbe.2022140CrossRefGoogle ScholarPubMed
Sahoo, S. P., Das, B., Pati, B. B., Garcia Marquez, F. P. and Segovia Ramirez, I., “Hybrid path planning using a bionic-inspired optimization algorithm for autonomous underwater vehicles,” J. Mar. Sci. Eng. 11(4), 761 (2023).10.3390/jmse11040761CrossRefGoogle Scholar
Liu, S., Wang, D. and Poh, E. K., “A Nonlinear Observer for Auvs in Shallow Water Environment,” In: IEEE/RSJ International Conference On Intelligent Robots and Systems (IROS) (IEEE Cat. No. 04CH37566), vol. 2 (IEEE, 2004) pp. 1130–1135.Google Scholar
Liu, S., Wang, D. and Poh, E. K., “Nonlinear Adaptive Observer Design for Tracking Control of Auvs in Wave Disturbance Condition,” In: OCEANS 2006-Asia Pacific (IEEE) pp. 1–8.Google Scholar
Li, J., Huang, H., Wan, L., Zhou, Z. and Xu, Y., “Hybrid strategy-based coordinate controller for an underwater vehicle manipulator system using nonlinear disturbance observer,” Robotica 37(10), 1710–1731 (2019). doi: 10.1017/S0263574719000213.CrossRefGoogle Scholar
Mukherjee, K., Kar, I. N. and Bhatt, R. K. P., “Ocean waves: A disturbance observer approach for an autonomous underwater vehicle,” OCEANS 2019-Marseille (IEEE, 2019).10.1109/OCEANSE.2019.8867514CrossRefGoogle Scholar
Naeem, W., Sutton, R. and Ahmad, S. M., “Pure Pursuit Guidance and Model Predictive Control of an Autonomous Underwater Vehicle for Cable/Pipeline Tracking,” In: Proceedings-Institute of Marine Engineering Science and Technology Part C Journal of Marine Science and Environment (Citeseer) pp. 25–35.Google Scholar
Naeem, W., Sutton, R., Chudley, J., Dalgleish, F. R. and Tetlow, S., “A genetic algorithm-based model predictive control autopilot design and its implementation in an autonomous underwater vehicle,” Proc. Inst. Mech. Eng., Part M: J. Eng. Maritime Environ. 218(3), 175–188 (2004).Google Scholar
Naeem, W., Sutton, R., Chudley, J., Dalgleish, F. R. and Tetlow, S., “An online genetic algorithm based model predictive control autopilot design with experimental verification,” Int. J. Control 78(14), 1076–1090 (2005).10.1080/00207170500228483CrossRefGoogle Scholar
Chen, Q., Chen, T. and Zhang, Y., “Research of GA-Based PID for AUV Motion Control,” In: International Conference on Mechatronics and Automation (IEEE, 2009) pp. 4446–4451.10.1109/ICMA.2009.5246715CrossRefGoogle Scholar
McGookin, E. W., “Auv sliding mode autopilot optimisation using genetic algorithms,” IFAC Proc. 34(7), 317–322 (2001).10.1016/S1474-6670(17)35102-9CrossRefGoogle Scholar
Vasudev, K., Sharma, R. and Bhattacharyya, S., “Shape optimisation of an auv with ducted propeller using ga integrated with cfd,” Ships Offshore Struct. 13(2), 194–207 (2018).10.1080/17445302.2017.1351292CrossRefGoogle Scholar
Abbasi, S., Zeinali, M. and Nejadabbasi, P., “Autonomous underwater vehicle hull geometry optimization using a multi-objective algorithm approach,” Int. J. Eng. 31(9), 1593–1601 (2018).Google Scholar
Tran, N. H., Nguyen, A. D. and Nguyen, T. N., “A Genetic Algorithm Application in Planning Path Using b-Spline Model for Autonomous Underwater Vehicle (auv),” In: Applied Mechanics and Materials, vol. 902 (Trans Tech Publications, 2020) pp. 54–64.Google Scholar
Safonov, M. G. and Tsao, T. C., “The Unfalsified Control Concept: A Direct Path From Experiment to Controller,” In: Feedback Control, Nonlinear Systems, and Complexity (Springer, 1995) pp. 196–214.10.1007/BFb0027678CrossRefGoogle Scholar
Tsao, T. C. and Safonov, M. G., “Unfalsified direct adaptive control of a two-link robot arm,” Int. J. Adapt. Control 15(3), 319–334 (2001).10.1002/acs.669CrossRefGoogle Scholar
Safonov, M. G. and Cheong, S. Y., “Unfalsified Control: Theory and Applications,” In: IEEE Conf. On Decision and Control, San Diego, CA December.Google Scholar
Safonov, M. G., Data-Driven Robust Control Design: Unfalsified ControlTechnical report (University of Southern California Los Angeles Dept of Electrical Engineering, 2006),Google Scholar
Manuelli, C., Cheong, S. G., Mosca, E. and Safonov, M. G., “Stability of Infalsified Adaptive Control with Non-Scli Controllers and Related Performance Under Different prior knowledge,” In: European Control Conference (ECC) (IEEE, 2007) pp. 702–708.Google Scholar
Paul, A., Stefanovic, M., Safonov, M. G. and Akar, M., “Multi-Controller Adaptive Control (MCAC) for a Tracking Problem Using an Unfalsification Approach,” In: Proceedings of the 44th IEEE Conference on Decision and Control (IEEE, 2005) pp. 4815–4820.10.1109/CDC.2005.1582923CrossRefGoogle Scholar
Cabral, F. B. and Safonov, M. G., “Unfalsified Model Reference Adaptive Control Using the Ellipsoid Algorithm,” In: Proceedings of IEEE Conference on Decision and Control (2013).Google Scholar
Previdi, F., Schauer, T., Savaresi, S. M. and Hunt, K. J., “Data-driven control design for neuroprostheses: a virtual reference feedback tuning (vrft) approach,” IEEE Trans. Control Syst. Tech. 12(1), 176–182 (2004).Google Scholar
Wang, J., Wang, Y., Cao, L. and Jin, Q., “Adaptive iterative learning control based on unfalsified strategy for chylla-haase reactor,” IEEE/CAA J. Autom. Sin. 1(4), 347–360 (2014).Google Scholar
Hassani, V., Onstein, T. F. and Pascoal, A. M., “Application of data-driven control to dynamic positioning,” IFAC-PapersOnLine 50(1), 12392–12397 (2017).10.1016/j.ifacol.2017.08.2505CrossRefGoogle Scholar
Das, J., Py, Fédéric, Harvey, J. B. J., Ryan, J. P., Gellene, A., Graham, R., Caron, D. A., Rajan, K. and Sukhatme, G. S., “Data-driven robotic sampling for marine ecosystem monitoring,” Int. J. Robot. Res. 34(12), 1435–1452 (2015).10.1177/0278364915587723CrossRefGoogle Scholar
Fossum, T. O., Intelligent autonomous underwater vehicles: A review of auv autonomy and data-driven sample strategies, Applied Underwater Robotic Laboratory Norwegian University of Science and Technology (2016).Google Scholar
Sanchez-Pena, R. S., Colmegna, P. and Bianchi, F., “Unfalsified control based on the $H_{\infty }$ controller parameterisation,” Int. J. Syst. Sci. 4(1) (2013).Google Scholar
Sung, Y. C., Patil, S. V. and Safonov, M. G., “Data-driven $h_{\infty }$ loop-shaping controller design,” Int. J. Robust Nonlinear 28, 3678–3693 (2018).10.1002/rnc.3612CrossRefGoogle Scholar
Patil, S. V., Sung, Y. C. and Safonov, M. G., “Nonlinear unfalsified adaptive control with bumpless transfer and reset,” IFAC-PapersOnLine 49(18), 1066–1072 (2016).Google Scholar
Sung, Y. C., Patil, S. V. and Safonov, M. G., “Robustness of uncertain switching nonlinear feedback systems against large time-variation,” IEEE Trans. Autom. Control 67(2), 993–1000 (2022).10.1109/TAC.2021.3077456CrossRefGoogle Scholar
Patil, S. V., Sung, Y. C. and Safonov, M. G., “Unfalsified adaptive control for nonlinear time-varying plants,” IEEE Trans. Autom. Control 67(8), 3892–3904 (2022).10.1109/TAC.2021.3110434CrossRefGoogle Scholar
Fossen, T. I.. Guidance and Control of Ocean Vehicles (John Wiley and Sons, 1994).Google Scholar
Ma, C., Tang, Y., Wang, J., Yan, J., Liao, Y. and Jiang, D., Adaptive trajectory tracking control and parameter identification for autonomous underwater vehicles based on contraction theory (2022).10.21203/rs.3.rs-1646233/v1CrossRefGoogle Scholar
Fossen, T. I., Handbook of Marine Craft Hydrodynamics and Motion Control (John Wiley and Sons, 2011).Google Scholar
Safonov, M. G., “Focusing On the Knowable,” In: Control Using Logic-Based Switching (Springer, 1997) pp. 224–233.10.1007/BFb0036098CrossRefGoogle Scholar
Wright, A. H., “Genetic Algorithms for Real Parameter Optimization,” In: Foundations of Genetic Algorithms, vol. 1 (Elsevier, 1991) pp. 205–218.Google Scholar
Hespanha, J. P.. Logic-Based Switching Algorithms in Control (Yale University, 1998).Google Scholar
Siek, Y., Zhurba, N. and Khutornaia, E., “Onboard computing environment of autonomous unmanned underwater vehicles: Possible design technologies and their comparative analysis,” Vibroeng. Procedia 38, 62–67 (2021).10.21595/vp.2021.22048CrossRefGoogle Scholar
Singh, S., Kaur, J. and Sinha, R. S., “A Comprehensive Survey on Various Evolutionary Algorithms on GPU,” In: International Conference on Communication, Computing & Systems (ICCCS–2014) pp. 20–21.Google Scholar
Wong, M. L., Wong, T. T. and Fok, K. L., “Parallel Evolutionary Algorithms On Graphics Processing Unit,” In: IEEE Congress On Evolutionary Computation, vol. 3 (IEEE, 2005) pp. 2286–2293.Google Scholar
Maitre, O., Baumes, L. A., Lachiche, N., Corma, A. and Collet, P., “Coarse grain Parallelization of Evolutionary Algorithms on GPGPU Cards with EASEA,” In: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, pp. 1403–1410.Google Scholar
Tsutsui, S. and Fujimoto, N., “Solving Quadratic Assignment Problems by Genetic Algorithms with GPU Computation: A Case Study,” In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers, pp. 2523–2530.Google Scholar
Pospichal, P., Jaros, J. and Schwarz, J., “Parallel Genetic Algorithm on the Cuda Architecture,” In: European Conference on the Applications of Evolutionary Computation (Springer) pp. 442–451.10.1007/978-3-642-12239-2_46CrossRefGoogle Scholar
Luo, Z. and Liu, H.. “Cellular Genetic Algorithms and Local Search for 3-Sat Problem on Graphic Hardware,” In: IEEE International Conference on Evolutionary Computation (IEEE, 2006) pp. 2988–2992.Google Scholar
Van Luong, T., Melab, N. and Talbi, E. G., “Parallel Hybrid Evolutionary Algorithms On Gpu,” In: IEEE Congress On Evolutionary Computation (IEEE) pp. 1–8.Google Scholar
Debattisti, S., Marlat, N., Mussi, L. and Cagnoni, S., “Implementation of a Simple Genetic Algorithm within the Cuda Architecture,” In: The Genetic and Evolutionary Computation Conference.Google Scholar
Pospichal, P. and Jaros, J., “Gpu-based acceleration of the genetic algorithm,” GECCO Competition (2009).Google Scholar
Hofmann, J., Limmer, S. and Fey, D., “Performance investigations of genetic algorithms on graphics cards,” Lect. Notes Comput. Sci. 12, 33–47 (2013).Google Scholar
Ou, J., Hong, S. H., Ziehl, P. and Wang, Y., “Gpu-based global path planning using genetic algorithm with near corner initialization,” J. Intell. Robot. Syst. 104(2), 34 (2022).Google Scholar