In this paper, a Stackelberg game-based safe tracking control method is proposed to address obstacle avoidance and trajectory tracking challenges for an unmanned surface vehicle (USV) under strategic pursuit and harassment by hostile targets. Inspired by the artificial potential field approach, strategic interference caused by dynamic repulsive forces from hostile targets is modeled as follower behavior within the game framework. A hierarchical control strategy is then designed based on the Stackelberg game. The performance index functions are formulated separately for the follower (repulsive force) and the leader (control force). A non-quadratic control-input cost function is introduced to enforce saturation constraints on control inputs. Using the maximum principle, control strategies for the leader and the follower are developed to achieve a dynamic balance between obstacle-avoidance safety and trajectory tracking accuracy. To address the nonlinearity of the unknown system dynamics and external disturbances in the USV, an integral reinforcement learning algorithm is employed to design a neural network that directly approximates the optimal solution to the Stackelberg game. The proposed safe trajectory tracking control strategy ensures the boundedness of all signals in the closed-loop system and enables the USV to track reference trajectories despite hostile target interference. Finally, simulation studies are conducted. The results demonstrate the effectiveness of the proposed safe tracking control strategy for the USV.
WEN et al. (Sun,) studied this question.