Publication

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.