Toi uu hieu suat toc do dong co Servo DC su dung bo dieu khien PID ket hop mang no-ron

TL;DR

This paper proposes a hybrid PID neural network control method for DC servo motors to enhance speed stability and response, effectively handling nonlinearities and load variations in industrial applications.

Contribution

It introduces a real-time adaptive control approach combining PID and neural networks to improve servo motor speed performance under varying conditions.

Findings

Significant improvement in speed tracking accuracy.

Enhanced stability and quick response of the motor.

Elimination of overshoot and steady-state error.

Abstract

DC motors have been widely used in many industrial applications, from small jointed robots with multiple degrees of freedom to household appliances and transportation vehicles such as electric cars and trains. The main function of these motors is to ensure stable positioning performance and speed for mechanical systems based on pre-designed control methods. However, achieving optimal speed performance for servo motors faces many challenges due to the impact of internal and external loads, which affect output stability. To optimize the speed performance of DC Servo motors, a control method combining PID controllers and artificial neural networks has been proposed. Traditional PID controllers have the advantage of a simple structure and effective control capability in many systems, but they face difficulties when dealing with nonlinear and uncertain changes. The neural network is integrated to adjust the PID parameters in real time, helping the system adapt to different operating conditions. Simulation and experimental results have demonstrated that the proposed method significantly improves the speed tracking capability and stability of the motor while ensuring quick response, zero steady-state error, and eliminating overshoot. This method offers high potential for application in servo motor control systems requiring high precision and performance.