Adaptive Neural Network Control of ROV under Ocean Current Disturbance
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摘要: 针对水下遥控机器人(ROV)在模型参数不确定和海流扰动下的运动控制问题, 基于有限时间命令滤波和径向基(RBF)神经网络设计出一种自适应反步控制系统。首先, 基于马尔科夫过程构建随机海流模型, 并构建海流扰动下的ROV数学模型; 其次, 针对期望速度引入命令滤波技术, 以减少传统反步法迭代导数带来的计算量; 再次, 利用径向基神经网络对ROV模型的不确定项和外部未知扰动进行估计, 并设计自适应神经网络控制器; 最后, 利用李雅普诺夫稳定性理论证明了闭环控制系统的稳定性。仿真结果表明, 本文设计的控制器可以实现ROV航行的精确控制,并能够有效抑制模型的不确定项和海流扰动对ROV运动的影响。Abstract: Aiming at the motion control problem of remotely operated unmanned vehicles (ROV) under uncertain model parameters and ocean current disturbance, an adaptive back-stepping control system is designed based on the limited time command filter and feedback base Radial Basis Function (RBF) neural network. Firstly, construct a stochastic ocean current model based on the Markov process, and construct a ROV mathematical model of velocity under ocean current disturbance; secondly, introduce command filtering technology for the desired speed to reduce the amount of calculation caused by the iterative derivative of the traditional back-stepping method; thirdly, use radial basis neural network to estimate the uncertainty terms and external unknown disturbances of the ROV model, and design an adaptive neural network controller; finally, Lyapunov stability theory is used to prove the stability of the closed-loop control system. The simulation results show that the controller designed in this paper can achieve precise control of ROV navigation, and can realize the uncertainty term of the effective stagnation model and the impact of sea current disturbance on ROV motion.
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图 3 海流扰动下ROV实际路径和期望路径
Figure 3. Actual path and desired path of ROV under ocean current disturbance
表 1 控制系统性能量化分析
Table 1. Quantitative analysis of control system performance
性能参数 ${E_{MAX}}$ ${M_{MAE}}$ ${R_{RMSE}}$ $x/m$ 0.0 049 0.0 186 0.0 569 $y/m$ 0.0 032 0.0 150 0.0 506 $\psi /rad$ 0.0 001 0.0 223 0.1 416 
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表 2 RBF神经网络性能量化分析
Table 2. Quantitative analysis of RBF neural network performance
性能参数 ${E_{MAX}}$ ${M_{MAE}}$ ${R_{RMSE}}$ $ {\hat f_1} $ 0.0 717 0.1 034 0.4 411 $ {\hat f_2} $ 0.1 321 0.3 843 1.2 340 $ {\hat f_3} $ 0.0 395 0.2 476 1.0 525
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