机器人并联机构冗余驱动控制方法研究  

Research on the Control Method for Robot Parallel Mechanism with Actuation Redundancy

作　　者：方月[1] 刘建英[1] 

机构地区：[1]河南工程学院机械工程学院,郑州450007

出　　处：《控制工程》2016年第8期1167-1171,共5页Control Engineering of China

基　　金：河南省科技计划项目(142102210398)

摘　　要：并联机构的驱动冗余具有结构简单、刚度大、灵巧度好、工作空间大等优点,在各种机器人中的应用比较广泛。以一种新型并联机构为研究对象,基于RBF神经网络和滑模控制提出了一种控制方法。介绍了机器人并联机构的基本结构并建立了其动力学模型,同时得出机器人并联机构冗余驱动控制的关键在于驱动力矩τ和系统不确定总量D。基于神经网络自适应滑模控制实现了驱动力矩的控制,包括有界控制项和鲁棒抑制项的设计;另外,基于自适应滑模控制实现了系统不确定总量的控制。最后,进行了仿真实验。仿真结果表明,所述机器人并联机构冗余控制方法具有更好的轨迹跟踪效果、稳态跟踪精度、自适应性、鲁棒稳定性。The parallel mechanism with actuation redundancy is widely used in many kinds of robots because of its advantages such as simple structure, good stiffness and dexterity, large working space and so on. This paper presents a novel parallel mechanism based on RBF neural network and sliding mode. The basic structure of the robot parallel mechanism is introduced and its dynamic model is established. At the same time, the control key of robot parallel mechanism with actuation redundancy is the driving torque τ and the total system uncertainty D. The drive torque control is realized based on neural network adaptive sliding mode, including bounded control and robust inhibition design. In addition, the total system uncertainty control is also realized based on adaptive sliding mode. Finally, the simulation experiments are carried out. The results testify that the proposed control method for robot parallel mechanism with actuation redundancy can achieve better performance, such as trajectory tracking effect, steady tracking accuracy, adaptability and robust stability.

关 键 词：并联机构 RBF神经网络 滑模控制 动力学建模 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]