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.

In this paper, by incorporating the dynamic surface control technique into a neural network based adaptive control design framework, we develop a backstepping based adaptive control design approach for uncertain non-affine pure-feedback nonlinear systems. By using the dynamic surface control technique, the problem of ‘‘explosion of complexity’’ inherent in existing methods are eliminated effectively. Stability analysis shows that the uniform ultimate boundedness of all the signals in the closed-loop system can be guaranteed, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation results demonstrate the effectiveness of the proposed scheme.

We study the cooperative control problem for a group of autonomous surface vehicles (ASV) with uncertain dynamics. A new decentralized cooperative controller is developed for a group of underactuated surface vehicles by employing the neural network-based dynamic surface approach, graph theory and Lyapunov stability theory. Using this design, it does not require to calculate the numerical derivatives of the virtual control signals as in traditional backstepping-based design. The advantages of the proposed cooperative controller are that, in addition to achieve a desired formation, the unknown uncertain dynamics such as coriolis and centripetal force, hydrodynamic damping, unmodelled hydrodynamics, disturbances from environment can be compensated by on-line learning. An illustrative example is provided to demonstrate the effectiveness of the proposed approach.