基于混合阈值事件触发机制的船舶自适应神经滑模控制

针对存在时变干扰和通信受限的船舶艏向保持控制问题, 本文设计了一种基于混合阈值事件触发机制的船舶自适应神经滑模控制算法. 首先, 采用径向基函数神经网络(RBF-NNs)对控制系统的模型不确定部分进行在线逼近. 其次, 构造了一种比例积分滑模面和基于混合阈值参数的事件触发规则来降低时变干扰引起的系统抖振和减少传输信道的通信资源占用. 并且在理论上分析了事件触发技术能够增强闭环控制系统对低频扰动的抗扰性. 最后, 通过Lyapunov理论证明了所提控制算法能够保证所有误差信号满足半全局一致最终有界稳定(SGUUB). 在时变干扰条件下进行数值仿真, 并且与现有控制技术进行对比, 仿真结果表明, 所提控制算法能够明显减少系统抖振和控制输入频率.

Adaptive neural sliding mode control for USV with the hybrid threshold event-triggered mechanism

This paper investigates an adaptive neural sliding mode control algorithm for the heading keeping of the underactuated surface vessels (USV) in presence of the external disturbance and the communication constraint. In the proposed algorithm, the nonlinear term is approximated by using the radial basis function neural networks (RBF-NNs) neural networks. Furthermore, a proportional integral sliding mode technique and hybrid threshold event-triggered rule have been introduced to reduce the system chattering and channel resource occupancy, which caused by the external disturbance and system uncertainties. In addition, the principle of avoiding low frequency disturbance that uses an event triggered mechanism is analyzed. Finally, the semi-global uniform and ultimately bounded (SGUUB) stable has been proved by employing the Lyapunov theory. The numerical simulation has been carried out to verify the effectiveness of the proposed algorithm. Compared with the existing techniques, it can be noted that the proposed algorithm is with the advantages in aspects of the system chattering and the transmission frequency of the control command.