灰狼算法优化的PID控制器在实验焦炉温度控制中的应用

Application of PID controller optimized by gray wolf algorithm in experimental coke oven temperature control

针对焦化领域实验焦炉温度控制问题,提出了一种基于灰狼优化算法优化PID参数(GWO-PID)的方法。首先,将焦炉温度控制的数学模型,简化为一个二阶带有纯滞后的模型。然后,利用灰狼优化算法对PID控制器的参数进行优化,提高了控制器的控制精度和鲁棒性。实验结果表明,灰狼算法优化后的PID参数,有效克服传统PID的参数僵化,该方法满足中小型实验焦炉温度控制要求,大大减少响应时间,可以实现无震荡、无超调。

Aiming at the temperature control problem of experimental coke oven in the field of coking,a method for optimizing PID parameters based on gray wolf optimization algorithm(GWO-PID)is proposed.First,the mathematical model of coke oven temperature control is simplified to a second-order model with pure hysteresis.Then,the gray wolf optimization algorithm is used to optimize the parameters of the PID controller,which improves the control accuracy and robustness of the controller.The experimental results show that the optimized PID parameters of the gray wolf algorithm can effectively overcome the parameter rigidity of traditional PID,and the method meets the temperature control requirements of small and medium-sized experimental coke ovens,greatly reduces the response time,and can achieve no shock and no overshoot.