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A New Hybrid Position/Force Control Scheme for Coordinated Multiple Mobile Manipulators

机译：协调多移动机械手的新型混合位置/力控制方案

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摘要

In this paper, a new hybrid position/force control scheme is proposed for coordinated multiple mobile manipulators holding a rigid object. The problem of the controller design for multiple mobile manipulators is much complicated as compared to single mobile manipulator. Many of the position/force control schemes for coordinated multiple mobile manipulators assume exact knowledge of the dynamical model. But the dynamic model of the coordinated multiple mobile manipulators is highly uncertain and faces external disturbances, uncertain environment intervention, etc. Therefore, model-based controller is inadequate to deal with such uncertain systems. In the proposed scheme, the inefficiency of the model-based controller is recovered by combining with RBF neural network-based mode-free controller along with a compensation controller. RBF neural network is utilized to estimate the unmodeled dynamics of the system without requiring the offline learning. The compensation controller is utilized to neutralize the effects of the friction terms, external disturbances, and the network reconstruction error. The online adaptation of the weights and the parameter updates are utilized in the Lyapunov function to make the system to be stable. Furthermore, the proposed control scheme assures that both the position and the internal force trajectory errors converge asymptotically. To depict the adequacy of the proposed control scheme, simulation results are provided with different existing controllers in a comparative manner.
机译：在本文中，提出了一种新的混合位置/力控制方案，该方案用于协调多个固定刚性物体的移动机械手。与单个移动机械手相比，用于多个移动机械手的控制器设计问题要复杂得多。用于协调多个移动操纵器的许多位置/力控制方案都假定具有动力学模型的确切知识。但是，协调的多个移动机械手的动力学模型是高度不确定的，并且面临外部干扰，不确定的环境干预等。因此，基于模型的控制器不足以应对这种不确定的系统。在提出的方案中，通过结合基于RBF神经网络的无模式控制器和补偿控制器来恢复基于模型的控制器的效率低下。 RBF神经网络用于估计系统的非建模动态，而无需离线学习。补偿控制器用于抵消摩擦项，外部干扰和网络重构误差的影响。 Lyapunov功能利用权重的在线调整和参数更新来使系统稳定。此外，所提出的控制方案确保位置和内力轨迹误差都渐近收敛。为了描述所提出的控制方案的适当性，以比较的方式为不同的现有控制器提供了仿真结果。

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