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Motion/force control scheme for electrically driven cooperative multiple mobile manipulators

机译:电动/力控制方案,用于电动协同多个移动机械手

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This paper presents the motion and force control problem of rigid-link electrically driven cooperative mobile manipulators handling a rigid object. Although, the motion/force control problem of cooperative mobile manipulators has been enthusiastically studied. But there is little research on the motion/force control of electrically driven cooperative mobile manipulators. Due to the inclusion of the actuator dynamics with the manipulator's dynamics, the controller exhibits some important characteristics. For the electromechanical system, we have designed a novel controller at the dynamic level as well as at the actuator level. In the proposed control scheme, at the dynamic level, uncertain non-linear mechanical dynamics is approximated with a hybrid controller containing model-based control scheme combined with model-free neural network based control scheme together with an adaptive bound. The adaptive bound is used to suppress the effects of external disturbances, friction terms, and reconstruction error of the neural network. At the actuator level, for the approximation of the unknown electrical dynamics, the model-free neural network is utilized. The developed control scheme provides that the position tracking errors, as well as the internal force, converge to the desired levels. Additionally, direct current motors are also controlled in such a way that the desired currents and torques can be attained. In order to make the overall system to be asymptotically stable, online learning of the weights and the parameter adaptation of the parameters is utilized in the Lyapunov function. The superiority of the developed control method is carried out with the numerical simulation results and its superior robustness is shown in a comparative manner.
机译:本文介绍了处理刚性物体的刚性链路电动协同移动机械手的运动和力控制问题。尽管,合作移动操纵器的运动/力控制问题已经热衷于研究。但是对电动协作移动操纵器的运动/力控制几乎没有研究。由于将执行器动力学纳入机械手的动态,控制器具有一些重要的特征。对于机电系统,我们在动态水平以及执行器级别设计了一种新颖的控制器。在所提出的控制方案中,在动态水平下,不确定的非线性机械动态近似于包含基于模型的控制方案的混合控制器与基于模型的基于神经网络的控制方案与自适应绑定一起使用。自适应绑定用于抑制神经网络的外部干扰,摩擦术语和重建误差的影响。在致动器级,对于未知电动动力学的近似,利用无模型的神经网络。开发的控制方案规定了位置跟踪误差以及内部力,收敛到所需的级别。另外,还可以以这样的方式控制直流电动机,使得可以获得所需的电流和扭矩。为了使整体系统呈渐近稳定,在Lyapunov函数中使用在线学习权重和参数的参数调整。开发控制方法的优越性通过数值模拟结果进行,并且其优异的鲁棒性以比较方式示出。

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