School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China.
School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2023 Jun 15;23(12):5614. doi: 10.3390/s23125614.
This paper proposes a learning control framework for the robotic manipulator's dynamic tracking task demanding fixed-time convergence and constrained output. In contrast with model-dependent methods, the proposed solution deals with unknown manipulator dynamics and external disturbances by virtue of a recurrent neural network (RNN)-based online approximator. First, a time-varying tangent-type barrier Lyapunov function (BLF) is introduced to construct a fixed-time virtual controller. Then, the RNN approximator is embedded in the closed-loop system to compensate for the lumped unknown term in the feedforward loop. Finally, we devise a novel fixed-time, output-constrained neural learning controller by integrating the BLF and RNN approximator into the main framework of the dynamic surface control (DSC). The proposed scheme not only guarantees the tracking errors converge to the small neighborhoods about the origin in a fixed time, but also preserves the actual trajectories always within the prescribed ranges and thus improves the tracking accuracy. Experiment results illustrate the excellent tracking performance and verify the effectiveness of the online RNN estimate for unknown dynamics and external disturbances.
本文提出了一种学习控制框架，用于具有固定时间收敛和约束输出的机器人机械手的动态跟踪任务。与基于模型的方法不同，所提出的解决方案通过基于递归神经网络（RNN）的在线逼近器来处理未知的机械手动力学和外部干扰。首先，引入时变正切型障碍李雅普诺夫函数（BLF）来构造固定时间虚拟控制器。然后，将 RNN 逼近器嵌入闭环系统中，以补偿前馈回路中的集中未知项。最后，通过将 BLF 和 RNN 逼近器集成到动态表面控制（DSC）的主框架中，设计了一种新颖的固定时间、输出受限的神经学习控制器。所提出的方案不仅保证了跟踪误差在固定时间内收敛到原点的小邻域内，而且还保证了实际轨迹始终在规定的范围内，从而提高了跟踪精度。实验结果说明了该方法的优越跟踪性能，并验证了在线 RNN 估计对未知动力学和外部干扰的有效性。
