欠驱动船舶路径跟踪的强化学习迭代滑模控制

Iterative sliding mode control based on reinforced learning and used for path tracking of under-actuated ship

针对三自由度欠驱动船舶的路径跟踪问题,本文提出一种基于强化学习的自适应迭代滑模控制方法。该方法引入双曲正切函数对系统状态进行迭代滑模设计,并采用神经网络对控制参数进行优化,增强控制器的自适应性。通过定义一种控制量抖振测量变量和强化学习信号,实现对神经网络的结构和参数进行在线调整,能进一步抑制控制量的抖振作用。应用5446TEU集装箱船的数学模型进行控制仿真,结果表明所设计控制器能有效地处理风和流等外界扰动,具有较强的鲁棒性,与迭代滑模控制器相比舵角的抖振减小明显,控制舵角信号符合船舶的实际操作要求,更符合工程实际要求。

An adaptive iterative sliding mode control method based on reinforced learning was proposed !or the path tracking of a 3-DOF under-actuated ship. T he method introduces a hyperbolic tangent function to design the iterative sliding mode for system states and uses a neural network to optimize the control parameters to enhance the adaptivity of the controller. The structure and parameters of the neural network were adjusted online by defining a type of con-trol amount chattering measurement variable and reinforced learning signal,which could further inhibit the chatte-ring of the control amount. The mathematical model of a 5446T EU container ship was used for the controller and simulation. The results show that the designed controller can m a nage the wind, flow, and other external disturb-ances effectively; this,the controller has strong robustness. Compared with the iterative sliding mode controller,the chattering of the rudder angle is obviously reduced,and the control signal of the rudder angle complies with the ac-tual operation requirements of the ship and even more with the actual requirements of the project.