基于RBF神经网络参数自整定的AUV深度控制

杜 度

doi:10.11993/j.issn.2096-3920.2019.03.008

基于RBF神经网络参数自整定的AUV深度控制

doi: 10.11993/j.issn.2096-3920.2019.03.008

杜度

海军研究院, 北京, 100161

详细信息

作者简介:
杜度(1973-), 男, 高级工程师, 主要研究方向为水下无人航行器控制.

中图分类号: TJ630.33; U674.941; TP273
计量
- 文章访问数: 539
- HTML全文浏览量: 20
- PDF下载量: 322
- 被引次数: 0
出版历程
- 收稿日期: 2018-09-30
- 修回日期: 2019-05-13
- 刊出日期: 2019-06-30

Parameters Self-Tuning for Depth Control of AUV Based on RBF Neural Network

DU Du

Naval Research Academy, Beijing 100161, China

摘要

摘要: 为了保证自主水下航行器(AUV)能够精确潜入固定深度海域, AUV垂平面控制技术非常重要。在基于比例-积分-微分(PID)控制设计控制器的过程中, 为保证控制器能够较好地控制AUV跟踪指定轨迹, 需要对PID参数进行调整, 但参数设定需要反复尝试, 不仅耗费大量时间, 而且不能保障其最优效果。为解决这一问题, 提出了一种基于径向基函数(RBF)神经网络的参数自整定PID控制方法。首先建立AUV垂平面运动模型, 然后设计RBF神经网络结构, 基于梯度下降方法给出了RBF参数以及PID参数的迭代公式, 并设计离散式PID控制器, 最后通过数值仿真验证了所提方法的有效性。仿真结果说明, AUV可以在较短时间内达到指定深度, 且PID各参数均能完成自整定。
- 自主水下航行器 /
- 深度控制 /
- 径向基函数神经网络 /
- 比例-积分-微分控制 /
- 自整定
Abstract: The autonomous undersea vehicle(AUV) vertical plane control technology is very important while accurately diving into the sea at a fixed depth. In the process of designing the controller based on proportional-integral-deriva- tive(PID) control, the PID parameters need to be adjusted to guarantee that the controller can control the AUV to track specified trajectory accurately, but the parameter setting needs to be tried repeatedly, which consumes much time and cannot guarantee its optimal effect. In order to solve this problem, a PID control method based on radial basis function(RBF) neural network is proposed in this paper for parameter self-tuning. Firstly, a motion model of AUV vertical plane is established, and the RBF neural network structure is designed. Then, an iterative formula of RBF parameters and PID parameters is given based on the gradient descent method, and a discrete PID controller is designed. Finally, the effectiveness of the proposed control method is verified by numerical simulation. Simulation results show that AUV can reach specified depth in a short time, and the PID parameters can be self-tuned.
- autonomous undersea vehicle(AUV) /
- depth control /
- radial basis function (RBF) neural network /
- proportion-al-integral-derivative(PID) control /
- self-tuning

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参考文献(18)

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