Fuzzy Neural Networks-Based Intelligent Tracking Control for Flexible-Joint Industry Robotic Manipulator

Due to the unstructured environment and the complex target tasks, the traditional mechanism and control theory are not able to meet the requirements of the real-time high-accuracy control of the environment-adaptive robot systems. This paper focuses on flexible-joint industry robotic manipulator, which can effectively resist external forces and reduce the vibration. Fuzzy neural networks-based intelligent control is designed to achieve precise trajectory tracking while address dynamic uncertainty issues for flexible-joint i ndustry robotic manipulator. Subsequently, the ultimately uniformly boundedness of the closed-loop system is proven through Lyapunov's direct method. With the careful choice of parameters, the system's states can converge to a small neighborhood close to zero. Finally, several necessary experiments on Baxter platform are provided to validate the proposed control method. The superiority of this control method is shown through its comparison with both PD control and neural network control techniques.