计算机与现代化 ›› 2024, Vol. 0 ›› Issue (07): 100-105.doi: 10.3969/j.issn.1006-2475.2024.07.015

• 算法设计与分析 • 上一篇    下一篇

  基于CDKF-RBFPID的激光器恒流源控制器











  

  1. (江南大学教育部轻工过程先进控制重点实验室,江苏 无锡 214122)
  • 出版日期:2024-07-25 发布日期:2024-08-08
  • 基金资助:
    国家自然科学基金青年科学基金资助项目(6170185); 国家自然科学基金资助项目(61901206)

Laser Constant Current Source Controller Based on CDKF-RBFPID

  1. (Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China)
  • Online:2024-07-25 Published:2024-08-08

摘要: 激光器的稳定工作需要一个输出电流精度高和稳定的恒流源控制系统。针对激光器恒流源系统在噪声环境下输出精度差且使用PID算法参数难整定的问题,提出一种基于中心差分卡尔曼滤波(Center Differential Kalman Filter,CDKF)与改进的径向基函数(Radial Basis Function, RBF)神经网络自适应PID控制相结合的算法CDKF-RBFPID。通过CDKF更新恒流源系统的状态、协方差,从而滤除系统中的状态噪声和测量噪声。利用强化学习Actor-Critic框架调整RBF-PID参数,实现自适应参数调整。对恒流源系统输出电流和激光器输出功率进行对比实验,结果表明:CDKF-RBFPID算法能够有效降低噪声对系统的影响,恒流源输出电流精度以及激光器输出功率稳定性进一步提升,其中响应时间缩短了58.3%,稳态误差降低了71.4%,输出电流控制精度达到1%。

关键词: 恒流源, PID控制, 中心差分卡尔曼滤波, RBF神经网络, Actor-Critic

Abstract: Stable operation of lasers requires a constant current source control system with high precision and stable output current. An algorithm based on the combination of central differential Kalman filtering (CDKF) and improved radial basis function (RBF) neural network adaptive PID control, named CDKF-RBFPID, is proposed to address the problem that the output accuracy of laser constant current source system is poor in noisy environment and the parameters are difficult to be adjusted by PID algorithm. By using CDKF, we update the state and covariance of the constant current source system, so as to filter out the state noise and measurement noise in the system. To accomplish adaptive parameter tuning, the RBF-PID parameters are modified using the reinforcement learning Actor-Critic architecture. Comparing the output current of the constant current source system and the output power of a laser, the experimental results demonstrate that the CDKF-RBFPID method can effectively lessen the impact of noise on the system, further enhance the accuracy of the constant current source output current and the stability of the laser output power, with the response time improved by 58.3%, the steady-state error reduced by 71.4%, and the output current control accuracy reaching 1%.

Key words: constant current source, PID control, CDKF, RBF neural network, Actor-Critic

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