In this paper, a higher-order terminal sliding mode control is proposed for fault accommodation of a class of Lipschitz second-order nonlinear systems. This approach is designed based on a combining between a novel third-order fast terminal sliding mode surface (TOFTSMS), which is designed to preserve the merits of the PID sliding surface and the fast terminal sliding mode (FTSM) surface, and a continuous control law based on higher-order sliding mode (HOSM) control strategy. However, the proposed TOFTSMC requires an exact dynamics model of the system and the prior knowledge of the bounded value of the uncertainties and faults in the design. In order to exclude the requirements, an adaptive fuzzy neural network is integrated; yielding a novel adaptive fuzzy neural TOFTSMC (AFN-TOFTSMC). The proposed analytical results are then applied to the attitude control of a spacecraft. Simulation results clearly demonstrate the great performance of the proposed algorithm compared to other state-of-the-art methods.

针对一类Lipschitz二阶非线性系统的故障调节，提出了一种高阶终端滑模控制方法。这种方法是基于新型三阶快速终端滑模表面（TOFTSMS）和连续控制之间的结合而设计的，该三阶快速终端滑模表面是为了保留PID滑动表面和快速终端滑模（FTSM）表面的优点而设计的。基于高阶滑模（HOSM）控制策略的定律。但是，提出的TOFTSMC需要精确的系统动力学模型以及设计中不确定性和故障的有界值的先验知识。为了排除需求，集成了自适应模糊神经网络。产生了一种新型的自适应模糊神经TOFTSMC（AFN-TOFTSMC）。拟议的分析结果随后应用于航天器的姿态控制。仿真结果清楚地证明了与其他现有技术方法相比，该算法的出色性能。