  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.
