This paper focuses on fixed-time formation control (FTFC) of a fully-actuated surface vessel (FASV) considering complex unknowns, including fully unknown dynamics and disturbances, input saturation and time-varying delays. First, using prediction idea to address time delay, a novel state predictor (SP) strategy combining with state transformation (ST) technique is devised for each FASV to predict the evolution of system states such that fixed-time stability can be ensured while solving the delay problem. Besides, the uncertainties in the transformed system are attentively considered. In addition, aiming to distinctly identify complex unknowns, predictor-based neural network is injected into the foregoing delay processing method. Finally, using time base generator (TBG), a new adaptive terminal sliding mode (ATSM) is incorporated into FTFC strategy which in turn contributes to decreasing control inputs and acquiring smooth convergence process. Simulation results and comparisons are thoroughly provided to testify the effectiveness and superiority of the designed FTFC scheme.

本文着重于考虑复杂未知因素的全驱动水面舰艇 (FASV) 的固定时间编队控制 (FTFC)，包括完全未知的动力学和扰动、输入饱和和时变延迟。首先，利用预测思想解决时间延迟问题，针对每个FASV设计了一种新的状态预测器（SP）策略，结合状态变换（ST）技术来预测系统状态的演变，从而在解决问题的同时保证固定时间的稳定性。延迟问题。此外，还仔细考虑了转换系统中的不确定性。此外，为了清晰地识别复杂的未知数，将基于预测器的神经网络注入到上述延迟处理方法中。最后，使用时基生成器（TBG），一种新的自适应终端滑模 (ATSM) 被纳入 FTFC 策略，这反过来有助于减少控制输入并获得平滑的收敛过程。充分提供仿真结果和比较，以证明所设计的 FTFC 方案的有效性和优越性。