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

机译:电动协作多移动机械手的运动/力控制方案

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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:本文提出了处理刚性物体的刚性连杆电动协作移动机械手的运动和力控制问题。尽管,对协作移动机械手的运动/力控制问题已经进行了热烈的研究。但是,关于电动协作移动机械手的运动/力控制的研究很少。由于在执行器动力学中包括了执行器动力学,因此控制器具有一些重要的特性。对于机电系统,我们在动态级别和执行器级别都设计了一种新颖的控制器。在提出的控制方案中,在动态级别上,使用包含基于模型的控制方案和基于无模型神经网络的控制方案以及自适应边界的混合控制器来近似不确定的非线性机械动力学。自适应边界用于抑制外部干扰,摩擦项和神经网络重构误差的影响。在执行器级别,为了近似未知的电气动力学,使用了无模型神经网络。开发的控制方案提供了位置跟踪误差以及内力收敛到所需水平的功能。另外,还以这样的方式控制直流电动机,使得可以获得期望的电流和转矩。为了使整个系统渐近稳定,在Lyapunov函数中使用了权重的在线学习和参数的参数自适应。数值模拟结果表明了所开发控制方法的优越性,并以比较的方式显示了其优越的鲁棒性。

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