基于自适应滑模的欠驱动无人艇轨迹跟踪控制算法

针对欠驱动水面无人艇在航行过程中存在的海洋环境干扰、数学模型参数不确定、执行器故障等问题,提出了一种基于扰动观测器与神经网络技术的自适应滑模轨迹跟踪策略。在无人艇三自由度模型的基础上,结合视线制导率,提出了一种新的轨迹跟踪制导策略。采用自适应滑模控制技术设计了欠驱动无人艇轨迹跟踪控制器,有效地抑制了执行器衰减故障对无人艇控制系统的影响；同时运用了非线性扰动观测器和自适应径向基函数神经网络分别对无人艇受到的外界干扰和模型参数不确定性进行补偿和拟合,提高了控制系统的抗干扰能力。基于Lyapunov定理证明了所设计的控制系统的稳定性,并在MATLAB中进行了仿真测试。仿真结果表明,所提出的轨迹跟踪控制算法可以在较为复杂的环境下实现对欠驱动无人艇的精准控制；相较于对比算法,位置的平均跟踪误差减小了80%以上,具备较高的稳定性和鲁棒性。

Trajectory Tracking Control of Underactuated Unmanned Surface Vessel Based on Adaptive Sliding Mode Control

In this paper, an adaptive sliding mode control strategy based on nonlinear disturbance observer and radial basis function neural network is presented for trajectory tracking of underactuated unmanned surface vessel (USV) with model parameter uncertainties and unknown external time-varying disturbances. Specifically, a novel trajectory tracking guidance law is proposed based on the three degree of freedom model of USV and line of sight guidance law. Meanwhile, disturbance observers and radial basis function neural network are introduced to estimate the unknown external disturbance and model uncertainties, which effectively improve the robustness of the system. A trajectory tracking controller is designed subsequently by combining adaptive rate and sliding mode control to diminish the influence of actuator fault. It is proved that the control system is stable based on Lyapunov function methods. The simulation results illustrate that the designed control system can achieve precise control of underactuated USV in complex environments. Compared to the comparison methods, the average position tracking error is reduced by more than 80%, which verifies the stability and robustness of the proposed method.