| 摘要: |
| 目的 为了得到最优PID控制器的整定参数，满足印版滚筒温度实际控制要求，使温度控制具有更高的精确性和准确性。方法 提出一种把遗传算法、粒子群算法相结合的智能混合算法，用该算法对二自由度PID参数进行优化，并将该思想用于印版滚筒温度控制系统；基于Matlab进行仿真，并与文中其他整定方法相比较。结果 通过仿真验证，文中提出方法具有良好的控制效果，与其他方法相比，具有系统响应快、调节时间短、超调量小、鲁棒性好等优点，并具有极强的抗干扰能力。其目标值跟踪特性中调节时间为143.7 s，超调量为15%；其外扰动抑制特性中调节时间为57 s，超调量为2.5%，明显优于其他方法。结论 采用该融合算法可充分发挥2种算法的优点，具有优良的性能指标值，可更好地满足现场控制要求。 |
| 关键词: 印版滚筒 遗传算法 粒子群算法 二自由度 鲁棒性 |
| DOI：10.19554/j.cnki.1001-3563.2020.05.026 |
| 分类号:TP273 |
| 基金项目:国家自然科学基金（11502145，61074087，61703277） |
|
| Parameter Tuning of Two-degree-of-freedom PID controller for Plate Cylinder Temperature Based on GA-PSO |
|
WU Yan-kai, ZHANG Wei, WANG Ya-gang
|
|
University of Shanghai for Science and Technology, Shanghai 200093, China
|
| Abstract: |
| The work aims to obtain the optimal tuning parameters of PID controller and meet the actual temperature control requirements of the plate cylinder, so that the temperature control has higher accuracy and precision. An intelligent fusion algorithm was proposed by combining genetic algorithm (GA) with particle swarm optimization (PSO). The algorithm was used to optimize the parameters of two-degree-of-freedom PID controller for the plate cylinder temperature control system. The simulation based on Matlab was carried out and the proposed method was compared with other tuning methods herein. Through simulation verification, the proposed method had good control effects. Compared with other methods, the said method was characterized by fast system response, short adjustment time, small overshoot and good robustness, and it also had extremely strong anti-interference ability. The adjustment time in command tracking characteristics was 143.7 s and the overshoot was 15%. The adjustment time in the external disturbance rejection characteristic was 57 s and the overshoot was 2.5%. All the performance indexes were significantly better than those of other methods. The proposed fusion algorithm can fully exploit the advantages of the two algorithms. Featured by excellent performance indexes, it can better meet the on-site control requirements. |
| Key words: plate cylinder genetic algorithm particle swarm optimization two degrees of freedom robustness |
