IEEE Trans Cybern. 2022 Jul;52(7):5973-5983. doi: 10.1109/TCYB.2021.3064865. Epub 2022 Jul 4.
With the more extensive application of flexible robots, the expectation for flexible manipulators is also increasing rapidly. However, the fast convergence will cause the increase of vibration amplitude to some extent, and it is difficult to obtain vibration suppression and satisfactory transient performance at the same time. In order to deal with the problem, a fixed-time learning control method is proposed to realize the fast convergence. The constraint on system outputs, system uncertainty, and input saturation is addressed under the fixed-time convergence framework. A novel adaptive law for neural networks is integrated into the backstepping method, which enhances the learning rate of neural networks. The imposed constraint on the vibration amplitude is guaranteed by using the barrier Lyapunov function (BLF). Moreover, the chattering problem is addressed by approximating the sign function smoothly. In the end, some simulations have been carried out to show the effectiveness of the proposed method.
随着柔性机器人的更广泛应用，对柔性机械手的期望也在迅速增加。然而，快速收敛在某种程度上会导致振动幅度增加，并且很难同时获得振动抑制和满意的瞬态性能。为了解决这个问题，提出了一种固定时间学习控制方法来实现快速收敛。在固定时间收敛框架下解决了系统输出、系统不确定性和输入饱和的约束问题。将神经网络的一种新的自适应律集成到回溯法中，提高了神经网络的学习率。通过使用障碍李雅普诺夫函数 (BLF) 来保证对振动幅度的约束。此外，通过平滑逼近符号函数来解决抖振问题。最后，进行了一些仿真来验证所提出方法的有效性。
