基于RBF神经网络补偿的ROV运动控制算法
ROV Motion Control Algorithm Based on RBF Neural Network Compensation
张帅军 1刘卫东 1李乐 1柳靖彬 1郭利伟 1徐景明1
作者信息
- 1. 西北工业大学航海学院,陕西西安,710072
- 折叠
摘要
针对作业型遥控水下航行器(ROV)在模型参数不确定和外部环境干扰下的运动控制问题,提出了一种基于径向基函数(RBF)神经网络的自适应双环滑模控制策略.首先,对于ROV外环位置控制采用改进趋近律的积分滑模控制方法,对于ROV内环速度控制采用指数趋近律的积分滑模控制方法;其次,为进一步改善滑模控制的抖振问题,引入双曲正切函数作为滑模切换项;然后,利用RBF神经网络控制技术对ROV模型的不确定参数和外部扰动进行估计与补偿;最后,利用李雅普诺夫稳定性理论证明了整个闭环系统的稳定性,并对作业型ROV的运动控制进行了数值仿真.仿真结果验证了所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不确定参数和外部扰动对ROV运动的影响.
Abstract
In view of the motion control problem of the operation-type remotely operated vehicles(ROVs)under the uncertainty of model parameters and the disturbance of the external environment,an adaptive double-loop sliding mode control strategy based on radial basis function(RBF)neural network was proposed.Firstly,the integral sliding mode control method with an improved reaching law was adopted for controlling the position of the ROV's outer loop,and the integral sliding mode control method with an exponential reaching law was adopted for controlling the speed of the ROV's inner loop.Secondly,in order to further improve the chattering problem of sliding mode control,the hyperbolic tangent function was introduced as the sliding mode switching term.Subsequently,the RBF neural network control technology was used to estimate and compensate for the uncertain parameters and external disturbances of the ROV model.Finally,the stability of the whole closed-loop system was proved by using the Lyapunov stability theory,and the motion control of the operation-type ROV was simulated numerically.The simulation results verify that the controller designed in this paper can achieve precise control of ROV navigation and effectively suppress the influence of uncertain parameters of the model and external disturbances on ROV motion.
关键词
遥控水下航行器/运动控制/径向基函数/自适应双环滑模控制/神经网络Key words
remotely operated vehicle/motion control/radial basis function/adaptive double-loop sliding mode control/neural network引用本文复制引用
基金项目
国家自然科学基金(61903304)
中央高校基本科研业务费专项(3102020HHZY030010)
高等学校学科创新引智计划(111计划)(B18041.0)
出版年
2024
国家科技期刊平台
NSTL
万方数据