PID结合神经网络实时调参，提升直流伺服电机速度稳定性。
Toi uu hieu suat toc do dong co Servo DC su dung bo dieu khien PID ket hop mang no-ron
- 用神经网络动态调整PID参数，应对非线性与不确定干扰。
- 仿真与实验表明响应快、无超调、稳态误差为零。
- 适合高精度伺服系统，如机器人关节和电动车辆驱动。
直流电机广泛应用于从多自由度机械臂到家用电器及电动车、列车等工业场景中。其核心功能是基于预设控制策略，确保机械系统的稳定定位与速度性能。然而，内部与外部负载变化会显著影响电机输出稳定性，使实现最优速度性能面临挑战。为此，提出一种结合PID控制器与人工神经网络的控制方法。传统PID结构简单、控制有效，但在处理非线性与不确定性变化时表现受限；引入神经网络可实时调节PID参数，增强系统对不同工况的适应能力。仿真与实验结果表明，该方法显著提升了电机的速度跟踪能力与稳定性，同时保证快速响应、零稳态误差且消除超调。该方法在对高精度与高性能要求的伺服电机控制系统中具有广泛应用潜力。
原文摘要 · Abstract (English)
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.
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