Pursuit-Evasion Games of Marine Surface Vessels Using Neural Network-Based Control

In this work, pursuit-evasion (PE) games with marine surface vessels (MSVs) as pursuers are solved while considering velocity constraints and unknown dynamics simultaneously. Differentiable performance index functions are designed for PE games based on minimum and maximum approximation functions. Then, we can obtain the desired pursuit velocities for MSVs satisfying velocity constraints and evasion strategies by applying game theory. Neural networks (NNs) are established to approximate unknown dynamics, which is suitable to design NN-based control to ensure that all velocities of MSVs converge to their desired ones. Through rigorous Lyapunov analyses, it can be guaranteed that all convergence and weight errors are uniformly ultimately bounded. Simulation results and comparison with known dynamics are provided and analyzed, which show that the proposed NN-based PE game is effective for MSVs with velocity constraints and unknown dynamics.