Distributed Robust Learning Control for Multiple Unmanned Surface Vessels With Fixed-Time Prescribed Performance
研究了多艘无人水面艇在模型不确定、外部干扰和输入饱和下的分布式编队控制问题，提出一种结合有限时间滑模观测器和复合学习控制的方法，实现固定时间内的编队误差收敛。
This article investigates the distributed formation control problem for multiple unmanned surface vessels (USVs) with model uncertainties, exogenous disturbance, and input saturation. First, a distributed finite-time sliding mode observer is developed to obtain the desired trajectory of each USV. Then, a second-order differentiable continuous fixed-time prescribed performance function is applied to reconstruct the error model based on the reference signal. In addition, the unknown part and disturbance are simultaneously handled by the composite learning control method as well as input saturation, where the acrlong NN, disturbance observer, auxiliary system, and nonsingular fast terminal sliding mode technique are integrated. Moreover, it is proved that the formation error converges to a small neighbor of the origin in a finite time. Finally, the computational simulation examples are conducted to validate the feasibility and effectiveness of the proposed method.