Neural network velocity field control of robotic exoskeletons with bounded input
Neural network velocity field control of robotic exoskeletons with bounded input
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DOI：
10.1109/aim.2017.8014208
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
H. Jabbari;T. Narikiyo;M. Kawanishi
中科院分区：
文献类型：
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作者：
H. Jabbari;T. Narikiyo;M. Kawanishi
速度场控制是机器人系统运动控制的一种替代方法。当期望任务中的时间在感兴趣的应用中不是那么重要时，它比轨迹跟踪问题更有优势。近年来，这种控制策略在机器人辅助康复中成为一种很有前途的控制方法。因此，本文研究了具有动态不确定性的机器人外骨骼的变结构控制问题。现有的变结构控制方法大多需要一定的机器人动力学模型知识，在实际应用中往往难以准确辨识。因此，本文设计了一种自适应神经网络变结构控制方法来补偿动态不确定性。控制器给出先验有界的控制命令，以考虑执行器的饱和。通过对两自由度机器人外骨骼的仿真和实验研究，验证了控制器的性能。
Velocity field control (VFC) is an alternative approach for motion control of robotic systems. It has advantages over the trajectory tacking problem when the timing in the desired task is of less importance in the application of interest. Recently, this control strategy has been emerged as a promising control method in robot-aided rehabilitation. Therefore, this paper addresses the problem of VFC for robotic exoskeletons with dynamic uncertainties. Most existing VFC methods require some knowledge of the dynamic model of the robot, which is usually difficult in practice to precisely identify. Consequently, this paper design an adaptive neural network VFC method to compensate for the dynamic uncertainties. The controller gives a priori bounded control command in order to take into account the saturation of actuators. The controller performance is validated through simulation and experimental studies on a two-degree-of-freedom lower-limb robotic exoskeleton.