IEEE Trans Neural Netw Learn Syst. 2019 Dec;30(12):3611-3620. doi: 10.1109/TNNLS.2018.2869375. Epub 2018 Oct 19.
In this paper, the adaptive neural network (NN) tracking control problem is addressed for robot manipulators subject to dead-zone input. The control objective is to design an adaptive NN controller to guarantee the stability of the systems and obtain good performance. Different from the existing results, which used NN to approximate the nonlinearities directly, NNs are employed to identify the originally designed virtual control signals with unknown nonlinear items in this paper. Moreover, a sequence of virtual control signals and real controller are designed. The adaptive backstepping control method and Lyapunov stability theory are used to prove the proposed controller can ensure all the signals in the systems are semiglobally uniformly ultimately bounded, and the output of the systems can track the reference signal closely. Finally, the proposed adaptive control strategy is applied to the Puma 560 robot manipulator to demonstrate its effectiveness.
在本文中，针对存在死区输入的机器人机械手，研究了自适应神经网络（NN）跟踪控制问题。控制目标是设计自适应 NN 控制器，以保证系统的稳定性并获得良好的性能。与现有的直接使用 NN 来近似非线性的结果不同，本文使用 NN 来识别最初设计的虚拟控制信号中具有未知非线性项的部分。此外，还设计了一系列虚拟控制信号和实际控制器。自适应反推控制方法和 Lyapunov 稳定性理论被用来证明所提出的控制器可以确保系统中的所有信号都是半全局一致最终有界的，并且系统的输出可以紧密跟踪参考信号。最后，将所提出的自适应控制策略应用于 Puma 560 机器人机械手，以验证其有效性。
