Fixed-time Distributed Adaptive Neural Network Hybrid Optimal Output-Feedback Formation Control for Multi-Usv Systems

This study investigates the fixed-time distributed adaptive neural network (NN) hybrid optimized output-feedback formation control issue for multiple unmanned surface vehicle (multi-USV) systems. Under the differential graphical games theory, a novel fixed-time distributed adaptive NN hybrid optimal formation control strategy is developed via designing a NN state observer. The proposed distributed hybrid optimal control scheme consists of a distributed adaptive NN feedforward controller and a distributed error feedback optimized controller. The former is designed by adopting backstepping recursive control design algorithm to handle the nonlinear dynamics problem in multi-USV systems. The latter is developed based on differential graphical games to achieve the global optimization control performance. It is demonstrated that the proposed fixed-time distributed NN hybrid optimal formation control strategy can achieve the multi-USV systems formation control objectives. Meanwhile, it can obtain the global optimal control performance and reach Nash equilibrium in a fixed time. Finally, the computer simulation verified the validity of proposed fixed-time distributed optimal formation control strategy.