基于粒子群算法进行参数自我调整的广义预测控制算法在汽包液位控制中的应用

Generalized Predictive Control Based on PSO and Its Application in Drum Water Level Control

PID控制算法的学习性能及泛化性能取决于参数设置;在常规方法中,这些参数以固定值形式参与运算,而当面对复杂分布的数据集时,可能无法挑选出一组能够胜任各种分布情况的参数;因此,提出一种基于粒子群算法(particle swarm optimization,PSO)进行参数自我调整的广义预测控制算法(generalized predictive control,GPC);该算法根据工业锅炉现场环境的复杂性,机组参数的时变性,有效地解决了PID控制的控制精度受到限制的问题;着重讲述了汽包液位控制方案的设计,可看出PSO-GPC控制在汽包液位控制中的重要应用,其次,通过粒子群算法对参数进行辨识,并给出了仿真算例,参数辨识准确,最后用PID控制算法在液位控制中的仿真曲线与广义预测控制算法对锅炉汽包液位进行了仿真曲线进行了对比分析,可见广义预测控制增强了系统的快速性,稳定性好且抗干扰性强。

Hyper-parameters, which determine the ability of learning and generalization for PID control algorithm and usually fixed during training. Thus when PID is applied to complex system modeling, this parameters-fixed strategy leaves PID in a dilemma of selecting rigorous or slack parameters due to complicated distributions of sample dataset. Therefore, in this paper we proposed GPC algorithm based on PSO algorithm in which parameters are adaptive to sample dataset distributions. According to the complexity of environment and time-varying parameters, we solve the problem which control precision is restricted based on PID. Focuses on the design of the steam drum level control scheme, which can see the importance of its application in the drum level control based on PSO-GPC. Then, it identifies the parameters based on PSO algorithm and the simulation example is given, the parameter identification is accurate. Finally through the comparison of PID which in third reference and GPC on simulation and analysis shows that GPC can enhance the rapidity of the system and the stability is better.