Multi-Objective Multidisciplinary Design Optimization of a Robotic Fish System

Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research...

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Vydáno v:Journal of marine science and engineering Ročník 9; číslo 5; s. 478
Hlavní autoři: Chen, Hao, Li, Weikun, Cui, Weicheng, Yang, Ping, Chen, Linke
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 2021
Témata:
Adaptability
Algorithms
artificial neural network
Autonomous underwater vehicles
Back propagation
Back propagation networks
Biomimetics
Complex systems
Computational fluid dynamics
computational fluid dynamics (CFD)
Computer applications
Design
Design optimization
Equilibrium
Finite volume method
Fish
Fluid dynamics
Fluid flow
Fluid mechanics
Hydrodynamics
Mathematical models
multi-objective optimization
Multidisciplinary design optimization
Multiple objective analysis
Neural networks
optimal design
Optimization algorithms
robotic fish
Robotics
Robots
Simulation
Turbulence models
ISSN:2077-1312, 2077-1312
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Shrnutí:Biomimetic robotic fish systems have attracted huge attention due to the advantages of flexibility and adaptability. They are typically complex systems that involve many disciplines. The design of robotic fish is a multi-objective multidisciplinary design optimization problem. However, the research on the design optimization of robotic fish is rare. In this paper, by combining an efficient multidisciplinary design optimization approach and a novel multi-objective optimization algorithm, a multi-objective multidisciplinary design optimization (MMDO) strategy named IDF-DMOEOA is proposed for the conceptual design of a three-joint robotic fish system. In the proposed IDF-DMOEOA strategy, the individual discipline feasible (IDF) approach is adopted. A novel multi-objective optimization algorithm, disruption-based multi-objective equilibrium optimization algorithm (DMOEOA), is utilized as the optimizer. The proposed MMDO strategy is first applied to the design optimization of the robotic fish system, and the robotic fish system is decomposed into four disciplines: hydrodynamics, propulsion, weight and equilibrium, and energy. The computational fluid dynamics (CFD) method is employed to predict the robotic fish’s hydrodynamics characteristics, and the backpropagation neural network is adopted as the surrogate model to reduce the CFD method’s computational expense. The optimization results indicate that the optimized robotic fish shows better performance than the initial design, proving the proposed IDF-DMOEOA strategy’s effectiveness.
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ISSN:2077-1312
2077-1312
DOI:10.3390/jmse9050478