Vibration Control of a Constrained Two-Link Flexible Robotic Manipulator With Fixed-Time Convergence
针对柔性机械臂快速收敛导致振动加剧的问题，提出一种固定时间学习控制方法，在保证输出约束和输入饱和下实现快速收敛与振动抑制，对机器人控制领域的研究者有参考价值。
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.