基于参数自整定PID的水下滑翔机航向控制方法

水下滑翔机航向控制的精度对海洋目标观探测具有重要意义。现有的水下滑翔机航向控制技术以比例积分微分（proportional-integral-derivative，PID）为主。为保证水下滑翔机按照预期轨迹运动，PID控制器参数需要反复设定和调整，很难达到快速准确的控制效果。针对该问题，提出了一种基于径向基函数（radial basis function，RBF）神经网络的参数自整定PID航向控制方法。首先建立水下滑翔机水平面内运动模型，然后构建了RBF神经网络结构，并通过梯度下降法给出了神经网络参数以及PID参数的迭代公式。仿真结果表明，该方法相较于常规PID控制方法能在较短的时间内收敛，控制系统精度较高，同时控制器参数能够快速自整定。为今后的水下滑翔机航向控制器提供了设计参考。

A steering control method of underwater glider based on parameter self-tuning PID

The accuracy of steering control of an underwater glider is very important for ocean target detection. Current steering control of the underwater glider (UG) mainly uses proportional-integral-derivative (PID) controller. However, to ensure that the underwater glider moves in accordance, PID controller parameters need to be repeatedly set and adjusted, which makes it difficult to meet the requirements for fast and accurate control. To solve the problem, a parametric self-tuning PID control method based on the radial basis function (RBF) neural network was proposed. Firstly, the dynamic model of the underwater glider in the horizontal plane was established. Then, the RBF neural network structure was constructed, and the iterative formulas of neural network parameters and PID parameters were given by the gradient descent method. Simulation results show that compared with the conventional PID controller, this controller has shorter setting time, higher precision, and the parameters of the controller can be quickly self-tuned. It provides a reference for the design of the underwater glider steering controller in the future.