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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Kinematic Control of Redundant Manipulators Using Neural Networks
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Kinematic Control of Redundant Manipulators Using Neural Networks

机译：使用神经网络的冗余机械手运动学控制

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摘要

Redundancy resolution is a critical problem in the control of robotic manipulators. Recurrent neural networks (RNNs), as inherently parallel processing models for time-sequence processing, are potentially applicable for the motion control of manipulators. However, the development of neural models for high-accuracy and real-time control is a challenging problem. This paper identifies two limitations of the existing RNN solutions for manipulator control, i.e., position error accumulation and the convex restriction on the projection set, and overcomes them by proposing two modified neural network models. Our method allows nonconvex sets for projection operations, and control error does not accumulate over time in the presence of noise. Unlike most works in which RNNs are used to process time sequences, the proposed approach is model-based and training-free, which makes it possible to achieve fast tracking of reference signals with superior robustness and accuracy. Theoretical analysis reveals the global stability of a system under the control of the proposed neural networks. Simulation results confirm the effectiveness of the proposed control method in both the position regulation and tracking control of redundant PUMA 560 manipulators.
机译：冗余解决方案是机器人操纵器控制中的关键问题。作为用于时间序列处理的固有并行处理模型，循环神经网络（RNN）可能适用于机械手的运动控制。但是，开发用于高精度和实时控制的神经模型是一个具有挑战性的问题。本文指出了现有RNN解决方案的两个局限性，即位置误差累积和投影集上的凸约束，并提出了两个改进的神经网络模型来克服它们。我们的方法允许非凸集用于投影操作，并且在存在噪声的情况下，控制误差不会随时间累积。与大多数使用RNN来处理时间序列的工作不同，所提出的方法是基于模型的且无需训练，这使得可以以出色的鲁棒性和准确性实现对参考信号的快速跟踪。理论分析揭示了在所提出的神经网络的控制下系统的全局稳定性。仿真结果证实了所提出的控制方法在冗余PUMA 560机械手的位置调节和跟踪控制中的有效性。

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