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首页> 外文期刊>Applied Soft Computing >Adaptive neural controller for cooperative multiple robot manipulator system manipulating a single rigid object
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Adaptive neural controller for cooperative multiple robot manipulator system manipulating a single rigid object

机译：用于协作多机器人机械手系统操纵单个刚体的自适应神经控制器

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摘要

In this article, an adaptive neural controller is developed for cooperative multiple robot manipulator system carrying and manipulating a common rigid object. In coordinated manipulation of a single object using multiple robot manipulators simultaneous control of the object motion and the internal force exerted by manipulators on the object is required. Firstly, an integrated dynamic model of the manipulators and the object is derived in terms of object position and orientation as the states of the derived model. Based on this model, a controller is proposed that achieves required trajectory tracking of the object as well as tracking of the desired internal forces arising in the system. A feedforward neural network is employed to learn the unknown dynamics of robot manipulators and the object. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary offline learning. The adaptive learning algorithm is derived from Lyapunov stability analysis so that both error convergence and tracking stability are guaranteed in the closed loop system. Finally, simulation studies and analysis are carried out for two three-link planar manipulators moving a circular disc on specified trajectory.
机译：在本文中，开发了一种自适应神经控制器，用于协作多机器人机械手系统来承载和操纵一个常见的刚性物体。在使用多个机器人操纵器协调操纵单个物体的过程中，需要同时控制物体的运动和操纵器施加在物体上的内力。首先，根据对象位置和方向导出机械手和对象的集成动态模型，作为所导出模型的状态。基于该模型，提出了一种控制器，该控制器可实现对对象的所需轨迹跟踪以及对系统中产生的所需内力的跟踪。前馈神经网络用于学习机器人操纵器和物体的未知动力学。结果表明，神经网络可以通过自适应学习过程来应对未知的非线性，并且不需要初步的离线学习。自适应学习算法源自Lyapunov稳定性分析，因此在闭环系统中既可以保证误差收敛，又可以保证跟踪稳定性。最后，对在指定轨迹上移动圆盘的两个三连杆平面操纵器进行了仿真研究和分析。

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