摘要
石化工厂装置常使用PID回路控制,而常规的参数调整方法需要依赖人工经验选择PID参数,存在一定的局限性。为了解决这个问题,设计了一种基于遗传算法的自整定PID控制器预测方法。该方法首先利用遗传算法对PID控制器的参数进行优化,得到最佳的参数组合。然后,将这组参数应用到模型中,并利用模型来预测下一个时刻的系统输出。根据实际输出与预测输出之间的误差,通过调整PID控制器的参数来减小偏差。通过不断迭代和优化,最终获得具有良好控制性能的PID控制器。采用遗传算法进行参数优化,能够快速将被控对象调整到稳定状态,并明显提高整个系统的响应速度。同时,在该方法在软件仿真下,稳态误差和超调量都得到了有效降低。
Petrochemical plant units often use PID loop control,and the conventional parameter tuning method relies on manual experience to select PID parameters,which has certain limitations.To solve this problem,a genetic algorithm-based self-tuning PID controller prediction method is designed.The method first uses genetic algorithm to optimize the parameters of the PID controller to obtain the best combination of parameters.Then,this set of parameters is applied to a model and the model is used to predict the system output at the next moment.Based on the error between the actual output and the predicted output,the deviation is reduced by adjusting the parameters of the PID controller.Through continuous iteration and optimization,a PID controller with good control performance is finally obtained.Using genetic algorithm for parameter optimization can quickly adjust the controlled object to the stable state and significantly improve the response speed of the whole system.At the same time,the steady-state error and overshoot are effectively reduced under the method in software simulation.
作者
曾彬洋
Zeng Binyang(Southwest Branch of China Petroleum Engineering&Construction Corporation,Sichuan,610000)
出处
《当代化工研究》
CAS
2024年第2期187-190,共4页
Modern Chemical Research
关键词
遗传算法
PID
参数整定
仿真
Genetic algorithm
PID
parameter tuning
simulation
