基于LSTM的无人船轨迹跟踪滑模控制算法研究

针对无人船模型不确定项和外界环境干扰缺乏自适应能力而使无人船乘坐舒适性降低的问题，提出一种基于长短期记忆网络(LSTM)的无人船轨迹跟踪滑模控制算法。LSTM用于补偿无人船模型不确定项和外界环境干扰，从而抑制滑模控制的抖动现象。以一艘游船为基础建立了无人船数学模型，设计滑模轨迹跟踪控制器，同时引入LSTM神经网络对无人船数学模型中的不确定项及外界环境干扰进行控制补偿，并在三种轨迹下进行了MATLAB/Simulink仿真测试。测试结果表明，基于LSTM的滑模控制算法轨迹跟踪精度高于滑模控制算法，轨迹平均绝对误差最高减小62%，LSTM神经网络能显著提高无人船的抗干扰能力。

LSTM-based sliding mode trajectory tracking control algorithm for unmanned surface vehicles

The problem of reduced comfort in unmanned vessel rides due to the lack of adaptability to uncertain model parameters and external environmental disturbances is addressed. A trajectory tracking sliding mode control algorithm based on Long Short-Term Memory (LSTM) is proposed. LSTM is utilized to compensate for uncertain model parameters and external environmental disturbances, thereby mitigating the jitter phenomenon in sliding mode control. A mathematical model of an unmanned vessel is established based on a recreational boat, and a sliding mode trajectory tracking controller is designed. Additionally, an LSTM neural network is introduced to compensate for uncertainties in the unmanned vessel′s mathematical model and external environmental disturbances. Simulation tests are conducted using MATLAB/Simulink under three different trajectories. The results indicate that the LSTM-based sliding mode control algorithm achieves higher trajectory tracking accuracy compared to the sliding mode control algorithm, with a maximum reduction of 62% in average absolute trajectory error. The LSTM neural network significantly improves the unmanned vessel′s disturbance rejection capability.