How to improve the power generation of wave energy converters (WEC) has become one of the main research objectives in wave energy field. This paper illustrates a framework on the use of back propagation (BP) neural network in predicting capture power of the frustum of a cone shaped floating body. Mathematical model of single floating body is derived, and radius, semi-vertical angle, mass, submergence depth, power take-off (PTO) damping coefficient, and stiffness coefficient are identified as key variables. Commercial software ANSYS-AQWA is used for numerical simulations to obtain hydrodynamic parameters, and then capture power is calculated by these parameters. A database containing 100 samples is established by Latin hypercube sampling (LHS) method, and a simple feature study is conducted. A BP neural network model with high accuracy is designed and trained for predictions based on built database. The results show that forecasting results and desired outputs are in great agreement with error percentage not greater than 4%, correlation coefficient (CC) greater than 0.9, P value close to 1, and root mean square error (RMSE) less than 139 W. The proposed method provides a guideline for designers to identify basic parameters of the floating body and system damping coefficient.

Fish-like robot is a special autonomous underwater vehicle with broad application prospects. Some previous studies concentrated on the hydrodynamics of free-swimming fish-like robots. But the hydrodynamic performance of fish-like robot swimming with a tilt angle in constrained space has not been well studied, and the influence of environmental wave and current on its is also still unclear. In this paper, the experiment devices, including a physical fish-like robot, a hydrodynamics measurement platform, and a six-axis force sensor, are used to study the effect of attack angle and environmental condition on the hydrodynamics of near-surface swimming fish-like robot. Nine attack angles, five oscillating amplitudes, and three environmental conditions are analyzed in the experiments. It shows that thrust force decreases when caudal fin passes above water surface, but the increased difference between gravity force and buoyancy force will compensate the decreased force generated by caudal fin when fish-like robot swims with certain dive angle. The extra reaction force generated by solid bottom boundary will promote the thrust force and vertical force. The surface water wave condition or surface water current condition also has obvious effects on hydrodynamic performance. This paper provides a new perspective to the research on the hydrodynamic performance of fish-like robot and will do favor in the development of fish-like robot.

To achieve time–energy–impact multi-objective optimization in the trajectory control of underwater manipulators, this paper proposes a Fast Non-Dominated Sorting Tuna Swarm Optimization algorithm (FNS-TSO). The algorithm integrates a fast non-dominated sorting mechanism into the Tuna Swarm Optimization algorithm, improves initialization through Optimal Latin Hypercubic Sampling (OLHS) to enhance population distribution uniformity, and incorporates a nonlinear dynamic weight to refine the spiral foraging strategy, thereby strengthening algorithmic robustness. To verify FNS-TSO’s effectiveness, we conducted comparative evaluations using standard test functions against three established algorithms: Multi-Objective Particle Swarm Optimization (MOPSO), Multi-Objective Jellyfish Search Optimization (MOJSO), and the Non-Dominated-Sorting Genetic Algorithm (NSGA-II). Results demonstrate superior overall performance, particularly regarding convergence speed and solution diversity, with solution set distributions showing enhanced uniformity. In practical implementation, we applied FNS-TSO to the multi-objective optimization of an underwater manipulator using quintic spline curves for trajectory planning. Simulation outcomes reveal respective reductions of 11.03% in total operation time, 19.02% in energy consumption, and 24.69% in mechanical impacts, with the optimized manipulator achieving stable point-to-point motion transitions.