基于扩张观测器的欠驱动船舶轨迹跟踪低频学习自适应动态面输出反馈控制

针对速度不可测的三自由度欠驱动船舶轨迹跟踪控制问题,考虑船舶存在模型参数不确定项以及外界环境干扰未知情况,提出一种基于扩张观测器的欠驱动船舶轨迹跟踪低频学习自适应动态面输出反馈控制策略.该策略构造扩张观测器估计船舶速度向量,利用神经网络算法逼近模型参数不确定项,然后采用动态面控制技术避免对虚拟控制律直接求导,简化控制律计算过程,并引入低频增益学习技术消除外界扰动导致控制信号产生高频振荡,最后选取李雅普诺夫函数证明该控制律能够保证船舶跟踪闭环系统中所有误差信号一致最终有界.仿真结果表明,本文所设计控制器对船舶模型参数不确定项及外界环境干扰具有较强的鲁棒性,能够实现对船舶轨迹的有效跟踪.

Extended state observer based adaptive dynamic surface output feedback control for underactuated surface vessel trajectory tracking with low-frequency learning

To solve the trajectory tracking problem of 3 DOF underactuated surface vessel with immeasurable velocity vectors, an extended state observer based adaptive dynamic surface output feedback control method with low frequency learning is proposed in the presence of uncertain ship model parameters and unknown external environmental disturbances. In this strategy, the extended state observer is designed to estimate ship velocity vector, and a neural network is introduced to approximate the model uncertainty. Then the dynamic surface control technology is applied to avoid direct derivation of the virtual control law and simplify the control law calculation process, and the low frequency learning method is introduced to eliminate the high frequency oscillation control signal caused by external disturbance. Finally, the application of a Lyapunov function proves that all signals in the closed-loop tracking system can be guaranteed the uniformly ultimate boundedness by the proposed control law. Simulation results show that the controller has strong robustness against model uncertainty and unknown external environmental disturbances, as well as the ship trajectory can be tracked effectively.