Vibration and Trajectory Tracking Control of Engineering Mechanical Arm Based on Neural Network

Area of Science:

• Robotics
• Control Systems
• Artificial Intelligence

Background:

• Engineering mechanical arms often exhibit instability and path tracking errors due to external influences.
• Existing control methods like PID struggle with precise trajectory control in dynamic environments.

Purpose of the Study:

• To develop and validate a neural network-based control method for improving engineering mechanical arm vibration and trajectory control.
• To address limitations of conventional controllers in handling unknown dynamics and external disturbances.

Main Methods:

• A mechanical arm network was employed to learn unknown dynamic properties without prior training.
• An adaptive neural network control system was designed and its stability proven using Lyapunov's function.
• Simulations in Matlab/Simulink were used to model and validate control errors.

Main Results:

• The adaptive neural network controller demonstrated superior performance in maintaining the desired trajectory under external interference.
• Compared to PID control, the neural network method resulted in significantly smaller input torque fluctuations and reduced jitter.
• Control precision was enhanced, and the incidence of uncontrolled motion was decreased.

Conclusions:

• Adaptive neural network control offers a robust solution for precise trajectory management of engineering mechanical arms.
• This method effectively mitigates vibration and improves tracking accuracy in the presence of uncertainties and disturbances.
• The proposed control strategy outperforms traditional PID control for complex mechanical arm applications.