The control of an autonomous underwater vehicle (AUV) is regarded as a difficult challenge, owing to the nonlinear and uncertain dynamics of the AUV. In this work, Optimized neural network (NN) is integrated with the “second-order sliding mode control (SoSMC) approach” for control of yaw angle in AUV. More particularly, the positive gain of SoSMC is predicted by an optimized NN model, where the training is performed by a novel Sea Lion Distance-based FireFly algorithm via tuning the optimal weights. At last, the supremacy of the adopted model is validated under various measures. Accordingly, the RMSE values accomplished by the proposed model is 40.94%, 1.39%, 0.69%, 0.69% and 0.41% better than existing models like “GW-SMC, FF-SoSMC, SLnO-SoSMC, POA-SoSMC and GW-SoSMC”, respectively, for set point 1.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Price excludes VAT (USA)
Tax calculation will be finalised during checkout.
No new data were generated or analysed in support of this research.
Autonomous underwater vehicle
Center of buoyancy
Extended Kalman filter
Generalized super-twisting algorithm
Linear quadratic regulator
Linear matrix inequality
Proportional integral derivative
Radial basis function neural network
Sliding mode controller
Second-order sliding mode controller
Sea lion optimization
Time delay estimation
Unified modelling language
Batista P, Silvestre C, Oliveira P (2009) A sensor-based controller for homing of underactuated AUVs. IEEE Trans Rob 25(3):701–716
Bejarbaneh EY, Masoumnezhad M, Armaghani DJ, Pham BT (2020) Design of robust control based on linear matrix inequality and a novel hybrid PSO search technique for autonomous underwater vehicle. Appl Ocean Res. https://doi.org/10.1016/j.apor.2020.102231
Beno MM, Valarmathi IR, Swamy SM, Rajakumar BR (2014) Threshold prediction for segmenting tumour from brain MRI scans. Int J Imaging Syst Technol 24(2):129–137
Campos E, Chemori A, Creuze V, Torres J, Lozano R (2017) Saturation based nonlinear depth and yaw control of underwater vehicles with stability analysis and real-time experiments. Mechatronics 45:49–59
Cho GR, Li J-H, Park D, Jung JH (2020) Robust trajectory tracking of autonomous underwater vehicles using back-stepping control and time delay estimation. Ocean Eng. https://doi.org/10.1016/j.oceaneng.2020.107131
Cingireddy AR, Ghosh R, Melapu VK, Joginipelli S, Kwembe TA (2022) Classification of parkinson’s disease using motor and non-motor biomarkers through machine learning techniques. Int J Quant Struct Prop Relat (IJQSPR) 7(2):1–21
Cui R, Zhang X, Cui D (2016) Adaptive sliding-mode attitude control for autonomous underwater vehicles with input nonlinearities. Ocean Eng 123:45–54
Du H, Yu X, Chen MZQ, Li S (2016) Chattering-free discrete-time sliding mode control. Automatica 68:87–91
Elhaki O, Shojaei K (2020) A robust neural network approximation-based prescribed performance output-feedback controller for autonomous underwater vehicles with actuators saturation. Eng Appl Artif Intell 88:103382
Fister I, Fister I, Yang X-S, Brest J (2013) A comprehensive review of firefly algorithms. Swarm Evol Comput 13:34–46
Goel S (2014) Pigeon optimization algorithm: a novel approach for solving optimization problems, In: Proceedings of the international conference on data mining and intelligent computing (ICDMIC), New Delhi, pp 1–5
Guerrero J, Antonio E, Manzanilla A, Torres J, Lozano R (2018) Autonomous underwater vehicle robust path tracking: auto-adjustable gain high order sliding mode controller. IFAC-PapersOnLine 51(13):161–166
Hien NV, He NV, Diem PG (2018) A model-driven implementation to realize controllers for autonomous underwater vehicles. Appl Ocean Res 78:307–319
Kiran BK (2021) Indian music classification using neural network based dragon fly algorithm. J Comput Mech Power Syst Control 4(3):32–40
Kumar N, Rani M (2020) An efficient hybrid approach for trajectory tracking control of autonomous underwater vehicles. Appl Ocean Res 95:102053. https://doi.org/10.1016/j.apor.2020.102053
Li B, Su T-C (2016) Nonlinear heading control of an autonomous underwater vehicle with internal actuators. Ocean Eng 125:103–112
Li D-j, Chen Y-h, Shi J-G, Yang C-J (2015a) Autonomous underwater vehicle docking system for cabled ocean observatory network. Ocean Eng 109:127–134
Li S, Wang X, Zhang L (2015b) Finite-time output feedback tracking control for autonomous underwater vehicles. IEEE J Oceanic Eng 40(3):727–751
Listwan J, Pieńkowski K (2017) Control of five-phase induction motor with application of second-order sliding-mode Direct Field-Oriented method, In: Proceedings of the IEEE conference publications, pp 1–6
Liu L, Mei K, Ding S (2017) Controller design of second order sliding mode control systems with mismatached perturbations, In: Proceedings of the IEEE conference publications, pp 752–757
Londhe PS, Mohan S, Patre BM, Waghmare LM (2017) Robust task-space control of an autonomous underwater vehicle-manipulator system by PID-like fuzzy control scheme with disturbance estimator. Ocean Eng 139:1–13
Mahdi H, Al-Bander B, Alwan MH, Abood MS, Hamdi MM (2021) Vehicular networks performance evaluation based on downlink scheduling algorithms for high-speed long term evolution–vehicle. Int J Interact Mobile Technol 15(21):52–56
Masadeh R, Mahafzah B, Sharieh A (2019) Sea lion optimization algorithm. Int J Adv Comput Sci Appl 10:388–395
Mastan Sharif SK, Satya Prasad K, Venkata Rao D (2018) Adaptive decision feedback equalizer with dynamic principle for echo cancellation. Trans Inst Meas Control 40(16):4455–4471
Meherkandukuri (2021) Deep convolutional neural network for emotion recognition via EEG Signal. J Comput Mech Power Syst Control 4(2):18–24
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Mohan Y, Chee SS, Xin DKP, Foong LP (2016) Artificial neural network for classification of depressive and normal in EEG. In: Proceedings of the IEEE EMBS conference on biomedical engineering and sciences (IECBES)
Park J, Sung W (2016) FPGA based implementation of deep neural networks using on-chip memory only. In: Proceedings of the 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp 1011–1015. IEEE
Petrich J, Stilwell DJ (2011) Robust control for an autonomous underwater vehicle that suppresses pitch and yaw coupling. Ocean Eng 38(1):197–204
Priya PSL, Bandyopadhyay B (2017) Discrete time sliding mode control for uncertain Delta operator systems with infrequent output measurements. Eur J Control 33:52–59
Qianwen Z (2021) Integrating renewable energy sources in electric vehicles via optimization assisted model. J Comput Mech Power Syst Control 4(1)
Qiao L, Yi B, Wu D, Zhang W (2017) Design of three exponentially convergent robust controllers for the trajectory tracking of autonomous underwater vehicles. Ocean Eng 134:157–172
Roukhami M, Lazarescu MT, Gregoretti F, Lahbib Y, Mami A (2019) Very low power neural network FPGA accelerators for tag-less remote person identification using capacitive sensors. IEEE Access 7:102217–102231
Roy RG, Ghoshal D (2019) A novel adaptive second-order sliding mode controller for autonomous underwater vehicles. Adapt Behav 29(1):39–54
Shaik MS, Prasad KS, Shaik RA, Rao DV (2016) Acoustic echo cancellation using computationally efficient adaptive algorithm techniques. Aptikom J Comput Sci Inf Technol 1(2):57–62
Sinjari B, D’Addazio G, Traini T, Varvara G, Scarano A, Murmura G, Caputi SA (2019) 10-year retrospective comparative human study on screw-retained versus cemented dental implant abutments. J Biol Regul Homeost Agents 33:787–797
Tang Y (2016) Vehicle tracking and counting based on vehicle recognition". China Comput Commun J 13:97–98
Tang Y, Dai R, Xie Y (2020) Optimization of energy efficiency for fpga-based convolutional neural networks accelerator. J Phys Conf Ser 1487(1):012028
Tc SR, Ss TR, Subrahmanyam JBV (2019) Enhanced deep convolutional neural network for fault signal recognition in the power distribution system. J Comput Mech Power Syst Control 2:39–46
Tu Y, Sadiq S, Tao Y, Shyu ML, Chen SC (2019) A power efficient neural network implementation on heterogeneous fpga and gpu devices. In: 2019 IEEE 20th international conference on information reuse and integration for data science (IRI): pp 193–199. IEEE
Verma G (2022) Secure VM migration in cloud: multi-criteria perspective with improved optimization model. Wireless Pers Commun 124(1):75–102. https://doi.org/10.1007/s11277-021-09319-w
Verma G, Chakraborty R (2019) A hybrid privacy preserving scheme using finger print detection in cloud environment. Ing Des Syst d Inf 24(3):343–351
Wadi A, Mukhopadhyay S, Lee J-H (2019) A novel disturbance-robust adaptive trajectory tracking controller for a class of underactuated autonomous underwater vehicles. Ocean Eng 189:106377
Woods SA, Bauer RJ, Seto ML (2012) Automated ballast tank control system for autonomous underwater vehicles. IEEE J Oceanic Eng 37(4):727–739
Yang H, Wang C, Zhang F (2013) Brief paper: a decoupled controller design approach for formation control of autonomous underwater vehicles with time delays. IET Control Theory Appl 7(15):1950–1958
Yu C, Xiang X, Lapierre L, Zhang Q (2017) Nonlinear guidance and fuzzy control for three-dimensional path following of an underactuated autonomous underwater vehicle. Ocean Eng 146:457–467
The authors have not disclosed any funding.
Conflict of interest
No potential conflict of interest was reported by the author.
This paper does not contain any studies with human participants or animals performed by any of the authors.
Human or animals rights participants
This article does not contain any studies with human participants performed by any of the authors.
Informed consent was obtained from all individual participants included in the study.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Roy, R.G., Lakhekar, G.V. & Tanveer, M.H. Designing of neural network-based SoSMC for autonomous underwater vehicle: integrating hybrid optimization approach. Soft Comput 27, 3751–3763 (2023). https://doi.org/10.1007/s00500-022-07511-z
- Underwater vehicles
- Neural network
- FireFly algorithm
- SLD-FA algorithm