IEEE Trans Cybern. 2021 Oct;51(10):4796-4807. doi: 10.1109/TCYB.2020.3021069. Epub 2021 Oct 12.
This article develops an adaptive neural-network (NN) boundary control scheme for a flexible manipulator subject to input constraints, model uncertainties, and external disturbances. First, a radial basis function NN method is utilized to tackle the unknown input saturations, dead zones, and model uncertainties. Then, based on the backstepping approach, two adaptive NN boundary controllers with update laws are employed to stabilize the like-position loop subsystem and like-posture loop subsystem, respectively. With the introduced control laws, the uniform ultimate boundedness of the deflection and angle tracking errors for the flexible manipulator are guaranteed. Finally, the control performance of the developed control technique is examined by a numerical example.
本文为输入受限、模型不确定性和外部干扰下的柔性机械臂开发了一种自适应神经网络（NN）边界控制方案。首先，利用径向基函数 NN 方法解决未知输入饱和、死区和模型不确定性问题。然后，基于反推方法，采用两个具有更新律的自适应 NN 边界控制器，分别稳定相似位置环子系统和相似姿态环子系统。通过引入的控制律，保证了柔性机械臂的挠度和角度跟踪误差的一致有界性。最后，通过数值示例检验了所开发控制技术的控制性能。
