摘要
本文利用切片理论与Morison方程计算了双关节水下机械臂在水中运动时受到的附加质量力、水阻力和水体流动对机械臂产生的冲击力,在传统机械臂动力学模型的基础上建立了完整的双关节水下机械臂动力学模型。基于推导的水下机械臂动力学模型提出了一种RBF滑模控制策略,采用多个RBF神经网络对水下机械臂动力学模型中的不确定项进行分块逼近,并使用饱和函数改进了控制律。本文判别了控制系统的稳定性,进行了仿真对比试验,结果表明该控制算法优于传统滑模控制和常规RBF滑模控制算法。本文所提出的算法,将水下机械臂的关节响应时间缩短至1 s,并将平均稳态误差缩小至3×10^(-6) rad,还削弱了控制系统的抖振效应,满足水下机械臂的控制要求。
This paper uses the strip theory and Morison equation to calculate the additional mass force,the water resistance and the impact force generated by the water flow on the double-joint underwater manipulator moves in the water.Based on the traditional manipulator dynamic model,an RBF sliding mode control strategy for the underwater manipulator is proposed.Multiple RBF networks are used to approximate the uncertain parameters in the dynamic model by blocks,with a saturation function used to improve the control law.In this paper,the stability of the control system and simulation comparison are carried out.The simulation results show that the control algorithm is superior to the traditional sliding mode control and conventional RBF sliding mode control algorithm.The joint response time of the underwater manipulator is shortened to 1s,with the steady-state error reduced to 3×10^(-6) rad.It also weakens the buffeting effect of the control system and meets the control requirements for the underwater manipulator.
作者
赵伟
张晓晖
杨松楠
ZHAO Wei;ZHANG Xiaohui;YANG Songnan(Faculty of Automation and Information Engineering,Xi’an University of Technology,Xi’an 710048,China)
出处
《西安理工大学学报》
CAS
北大核心
2021年第4期555-561,共7页
Journal of Xi'an University of Technology
基金
国家自然科学基金资助项目(61873200)。
关键词
水下机械臂
动力学建模
RBF神经网络
滑模控制
underwater manipulator
dynamic modeling
RBF neural network
sliding mode control
