This paper presents mechatronic design and hierarchical locomotion control of a biomimetic robotic fish for three-dimensional swimming modes. Inspired by biological features of Lamprey, a closed loop sensory feedback Central Pattern Generator (CPG) model is adapted to hierarchical control mechanism in order to provide robust and effective biomimetic control structure. A sensory feedback mechanism plays an important role to react external stimuli from environment. In addition, a closed loop fuzzy logic control structure is developed as a brain model to decide adaptive swimming modes according to sensory information. In order to provide three-dimensional motion abilities, the Centre of Gravity (CoG) control mechanism is designed and controlled by a back-forth proportional control structure. Experimental results are obtained to prove the CPG-based closed loop sensory feedback control structure with the developed robotic fish prototype.

This work focuses on developing a complete non-linear dynamic model comprising entirely kinematic and hydrodynamic effects of Carangiform locomotion based on the Lagrange approach by adapting the parameters and behaviors of a real carp. In order to imitate biological features, swimming patterns of a real carp for forward, turning and up-down motions are analyzed by using the Kineova 8.20 software. The proportional optimum link lengths according to actual size, swimming speed, flapping frequency, proportional physical parameters and different swimming motions of the real carp are investigated with the designed robotic fish model. Three-dimensional (3D) locomotion is evaluated by tracking two trajectories in a MATLAB environment. A Reaching Law Control (RLC) approach for inner loop (Euler angles-speed control) and a guidance system for the outer loop (orientation control) are proposed to provide an effective closed-loop control performance. In order to illustrate the 3D performance of the proposed closed loop control system in a virtual reality platform, the designed robotic fish model is also implemented using the Virtual Reality Modeling Language (VRML). Simulation and experimental analysis show that the proposed model gives us significant key solutions to design a fish-like robotic prototype.

This paper presents mechatronic design and manufacturing of a biomimetic Carangiform-type autonomous robotic fish prototype (i-RoF) with two-link propulsive tail mechanism. For the design procedure, a multi-link biomimetic approach, which uses the physical characteristics of a real carp fish as its size and structure, is adapted. Appropriate body rate is determined according to swimming modes and tail oscillations of the carp. The prototype is composed of three main parts: an anterior rigid body, two-link propulsive tail mechanism, and flexible caudal fin. Prototype parts are produced with 3D-printing technology. In order to mimic fish-like robust swimming gaits, a biomimetic locomotion control structure based on Central Pattern Generator (CPG) is proposed. The designed unidirectional chained CPG network is inspired by the neural spinal cord of Lamprey, and it generates stable rhythmic oscillatory patterns. Also, a Center of Gravity (CoG) control mechanism is designed and located in the anterior rigid body to ensure three-dimensional swimming ability. With the help of this design, the characteristics of the robotic fish are performed with forward, turning, up-down and autonomous swimming motions in the experimental pool. Maximum forward speed of the robotic fish can reach 0.8516 BLs^-1 and excellent three-dimensional swimming performance is obtained.

Induction motors require effective speed controllers to handle challenging conditions such as indirect vector control, nonlinear dynamics, load-disturbances, and changes in rotor resistance. Although proportional–integral (PI) controllers and type-1 fuzzy logic controllers (T1-FLC) are relatively straightforward to implement, they can produce significant overshoot and slow recovery; type-2 fuzzy logic controllers (T2-FLC), on the other hand, improve uncertainty management at the cost of higher computational complexity. This study proposes a type-3 fuzzy logic controller (T3-FLC) that balances robustness with a single $\alpha$-slice using two inputs and seven membership functions per input (49 rules). In six comparison scenarios, the type-3 FLC (T3-FLC) consistently offers a lower overshoot percentage and shorter recovery/settling times than the PI controller and type-1 FLC (T1-FLC). Overshoot drops to $0.13\%$ with T3-FLC during a high-speed positive step, while this value for the PI controller is $4.43\%$. During a low-amplitude positive step, T3-FLC reaches $1.37\%$, while the PI controller reaches $11.12\%$ and T1-FLC reaches $4.13\%$. After load torque is removed, the recovery time $t_{\mathrm{rec}}$ under T3-FLC is $0.064$ s at high speed and $0.158$ s at low speed, while for PI, these values are $0.400$ s and $1.975$ s, respectively. Under variations in rotor resistance, T3-FLC maintains a significantly smaller overshoot value: with a $-20\%$ change (3–6 s window), the values are $1.45\%$ (T3-FLC) versus $9.59\%$ (PI) and $4.51\%$ (T1-FLC); with a $+20\%$ change (3–6 s), the values are $0.14\%$ (T3-FLC) versus $4.36\%$ (PI) and $0.15\%$ (T1-FLC). Although there are isolated cases in which PI or T1-FLC shows a marginal advantage in a single metric (e.g., slightly smaller overshoot during transition or lower peak error during disturbance), T3-FLC generally provides the best balance, combining low overshoot with short settling/recovery time while keeping steady-state error at zero in all scenarios.