多自主船协同路径跟踪的自适应动态面控制

为实现多自主船含模型不确定与未知风浪流干扰下的协同路径跟踪控制,提出了一种基于神经网络自适应动态面控制的协同路径跟踪算法.该算法采用单隐层(SHL)神经网络逼近模型不确定性以及海洋环境干扰,所引入的动态面设计技术显著降低了控制算法的复杂性.同时将网络通信约束考虑在内,通过设计分散式协同控制律有效地降低了信息通讯量.Lyapunov稳定性分析证明了闭环系统所有的状态和信号是有界的,并且通过选择合适的设计参数可使跟踪误差为任意小.对比仿真结果验证了所提方法的有效性.

Adaptive dynamic surface control for cooperative path following of multiple autonomous surface vessels

This paper addresses the cooperative path following problem of multiple autonomous surface vessels with model uncertainty and unknown disturbances induced by wind, waves and ocean currents. A cooperative path following algorithm is proposed based on the neural network adaptive dynamic surface control technique. The single hidden layer (SHL) neural network is employed to approximate the model uncertainty and ocean disturbances; the dynamic surface control technique is introduced to dramatically lower the complexity of this algorithm; and the decentralized cooperative control law is adopted to reduce the amount of communications. The Lyapunov stability analysis shows that all closedloop signals are uniformly ultimately bounded, and a small tracking error is achieved by appropriately choosing design parameters. Comparative studies demonstrate the effectiveness of the proposed method.