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Designing of neural network-based SoSMC for autonomous underwater vehicle: integrating hybrid optimization approach

  • Optimization
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Abstract

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.

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Data availability

No new data were generated or analysed in support of this research.

Abbreviations

AUV:

Autonomous underwater vehicle

CB:

Center of buoyancy

EKF:

Extended Kalman filter

FoSMC:

First-order SMC

FF:

Firefly algorithm

FC:

Fuzzy controller

GSTA:

Generalized super-twisting algorithm

LQR:

Linear quadratic regulator

LMI:

Linear matrix inequality

NN:

Neural network

PID:

Proportional integral derivative

RBF-NN:

Radial basis function neural network

SMC:

Sliding mode controller

SoSMC:

Second-order sliding mode controller

SLnO:

Sea lion optimization

TDE:

Time delay estimation

UML:

Unified modelling language

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Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, National Institute of Technology, Agartala, Barjala, Jirania, Agartala, Tripura, India

    Rupam Gupta Roy

  2. Department of Instrumentation Engineering, College of Engineering, Pune, Maharashtra, India

    Girish Vithalrao Lakhekar

  3. Department of Robotics and Mechatronics Engineering, Kennesaw State University, Marietta, GA, 30067, United States

    Muhammad Hassan Tanveer

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  1. Rupam Gupta Roy
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  2. Girish Vithalrao Lakhekar
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  3. Muhammad Hassan Tanveer
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Correspondence to Rupam Gupta Roy.

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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

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