Adaptive Dynamic Surface Control of Flexible-Joint Robots Using Self-Recurrent Wavelet Neural Networks
Adaptive Dynamic Surface Control of Flexible-Joint Robots Using Self-Recurrent Wavelet Neural Networks
复制标题
DOI：
10.1109/tsmcb.2006.875869
复制
发表时间：
2006-12
期刊：
影响因子：
--
通讯作者：
S. Yoo;Jin Bae Park;Y. Choi
中科院分区：
文献类型：
--
作者：
S. Yoo;Jin Bae Park;Y. Choi
提出了一种新的方法，用于机器人动力学和执行器动力学模型不确定性的柔性关节（FJ）机器人的鲁棒控制。该控制系统是自适应动态面控制（DSC）技术和自回归小波神经网络（SRWNN）的组合。自适应DSC技术提供了克服反推控制器中的“复杂性爆炸”问题的能力。SRWNN用于观测FJ机器人的任意模型不确定性，并在线训练其所有权值。从李雅普诺夫稳定性分析出发，导出了它们的自适应律，并证明了闭环自适应系统中所有信号的一致最终有界性。最后，以三连杆FJ机器人为例进行了仿真，验证了所提出的控制系统具有良好的位置跟踪性能和对负载不确定性及外部干扰的鲁棒性
A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system