作业型水下机器人纵、横倾姿态自适应区域控制方法

Adaptive Region Control Method of the Pitch and Roll Attitudes for Operating Autonomous Underwater Vehicle

本文主要研究作业型自主式水下机器人(AUV)的纵、横倾姿态自适应区域控制问题.在实际作业中,机械手作业干扰和环境不确定性等因素将影响作业过程的艇体姿态控制,进而影响运动、作业的精度.针对此姿态稳定性控制问题,提出一种基于RBF(径向基函数)神经网络的水下机器人姿态自适应区域控制方法.针对系统模型的不确定因素,采用RBF神经网络对其进行在线估计,引入滑模控制项对估计误差进行在线补偿;针对RBF神经网络控制参数的取值问题,设计网络权值、径向基中心与方差的在线调整律,对控制参数进行自适应学习,以适应机器人艇体的不同姿态变化;针对艇体姿态的快速稳定收敛需求,在区域控制器中加入PI(比例-积分)环节,缩短姿态调节时间、降低稳态误差.基于李亚普诺夫稳定性分析,从理论上证明区域控制误差一致渐近稳定.最后,通过作业型水下机器人样机的纵、横倾姿态控制实验,验证了本文方法的有效性.

The adaptive region control method of the pitch and roll attitudes for operating autonomous underwater vehicle(AUV) is addressed. In the actual operation process, there are some influencing factors such as the operation disturbance of manipulator and environmental uncertainty, which will affect the attitude control of the underwater vehicle, and then affect the movement and operation accuracy. To solve the attitude stability control problem, an adaptive region control method of AUV based on radial basis function(RBF) neural network is proposed. An RBF neural network is applied to estimating the uncertain factors of the dynamics model online, and the sliding mode control term is incorporated to compensate the estimation error online. Then, an online adjustment law of the network weights, the center of the radial basis function and the variance is designed to solve the value of the control parameters of RBF neural network, and self-adaptive learning of the control parameters is achieved to adapt to the attitude changes of the vehicle. Moreover, a PI(proportional-integral) section is added to the region controller to realize the fast and stable convergence of the attitude, and thus the adjustment time and steady state error are reduced. Based on the Lyapunov stability analysis, it is theoretically proved that the region control error is uniformly asymptotically stable. Finally, the pitch and roll attitude control experiments of the operating AUV prototype are conducted to verify the effectiveness of the proposed method.