基于PNDSC 的欠驱动AUV编队控制器设计

针对含有模型不确定与未知海洋环境扰动下的欠驱动自主水下航行器(AUV)的编队控制问题, 提出一种基于预估器的神经网络动态面(PNDSC) 控制算法. 将动态面法引入控制器的设计中, 采用神经网络逼近AUV模型中的不确定项与海洋环境的扰动, 并结合预估器设计了神经网络权值的离散迭代更新率. Lyapunov 稳定性分析表明, 闭环系统所有信号是一致最终有界的. 仿真结果验证了所提出方法的有效性.

Formation controller design based on PNDSC for underactuated AUV

For the formation control problem of underactuated autonomous underwater vehicles(AUV) subject to dynamical uncertainty and unknown ocean disturbances, a formation control algorithm is proposed based on a predictor-based neural dynamic surface control(PNDSC) method. The dynamic surface control technique is introduced into the controller design, and neural networks are employed to approximate the dynamical uncertainty and ocean disturbance. In addition, the prediction errors are used to update the neural iterative updating laws. Lyapunov stability analysis demonstrates that all signals in the closed-loop are uniformly ultimately bounded. Simulation results show the effectiveness of the proposed method.