This paper introduces a novel Nussbaum function-based PID control for robotic manipulators. The integration of the Nussbaum function into the PID framework provides a solution with a simple structure that effectively tackles the challenge of unknown control directions. Stability is achieved through a combination of neural network-based estimation and Lyapunov analysis, facilitating automatic gain adjustment without the need for system dynamics. Our approach offers a gain determination with minimum parameter requirements, significantly reducing the complexity and enhancing the efficiency of robotic manipulator control. The paper guarantees that all signals within the closed-loop system remain bounded. Lastly, numerical simulations validate the theoretical framework, confirming the effectiveness of the proposed control strategy in enhancing robotic manipulator control.

翻译：本文提出了一种新颖的基于Nussbaum函数的PID控制方法，用于机器人操作器。将Nussbaum函数集成到PID框架中，提供了一种结构简单的解决方案，有效应对未知控制方向带来的挑战。通过结合神经网络估计与Lyapunov分析实现系统稳定性，无需依赖系统动力学即可完成自动增益调节。本方法在参数需求最小化的前提下实现增益确定，显著降低了机器人操作器控制的复杂度并提升了效率。论文保证闭环系统内所有信号均保持有界。最后，数值仿真验证了理论框架，证实了所提控制策略在增强机器人操作器控制方面的有效性。