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Genetic Algorithm Optimized Grey-Box Modelling and Fuzzy Logic Controller for Tail-Actuated Robotic Fish

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Abstract

The development of bio-inspired aquatic robots for underwater operations and scientific research has dramatically improved over the past decades. Dynamic modelling of such robots relies on the reactive force produced through the physical movement of their body parts. It is highly complex to capture the complete hydrodynamics for defining the reactive force, making the modelling and control of robotic fish challenging. This paper captures the hydrodynamic parameters from real-time data using a grey-box model structure optimized by a genetic algorithm (GA). The GA-optimized model aligns with real-time experimentation, exhibiting an average mean square error of approximately 0.001 m2 across six swimming speeds. Next, GA optimization is proposed for designing the fuzzy logic controller to control the speed of the robotic fish. GA adjusts the parameters of the membership function and minimizes the error function. Finally, the controller’s performance is compared with the classical GA-tuned PID (GAPID) and conventional fuzzy logic controller (FLC). The proposed controller has significant improvement in terms of tracking error, integral square error (ISE), integral absolute error and integral time absolute error. The optimized controller has achieved an ISE improvement of 84.64% and 87.15% compared to FLC and GAPID controllers, respectively.

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The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. School of Electrical and Electronics Engineering, SASTRA Deemed University, Thanjavur, 613 401, India

    Palmani Duraisamy, Manigandan Nagarajan Santhanakrishnan & Rengarajan Amirtharajan

  2. Center for Computational Engineering and Networking, School of Artificial Intelligence, Amrita Vishwa Vidyapeetham University, Coimbatore, 641 112, Tamil Nadu, India

    Palmani Duraisamy

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  1. Palmani Duraisamy
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Contributions

Conceptualization, Methodology, Writing - original draft (PD); Writing - review and editing, Validation, Supervision (MNS); Formal Analysis, Visualization, Supervision (RA); All authors reviewed the manuscript.

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Correspondence to Manigandan Nagarajan Santhanakrishnan.

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Duraisamy, P., Santhanakrishnan, M.N. & Amirtharajan, R. Genetic Algorithm Optimized Grey-Box Modelling and Fuzzy Logic Controller for Tail-Actuated Robotic Fish. Neural Process Lett 55, 11577–11594 (2023). https://doi.org/10.1007/s11063-023-11391-1

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