基于蚁群算法的改进广义预测控制及参数优化  

Improved Generalized Predictive Control and Parameter Optimization Based on Ant Colony Algorithm

作　　者：张悦[1] 何同祥[1] Zhang Yue;He Tongxiang(Automation Department,North China Electric Power University,Hebei,Baoding,071000,China)

机构地区：[1]华北电力大学自动化系,河北保定071000

出　　处：《仪器仪表用户》2021年第9期27-29,共3页Instrumentation

摘　　要：本文针对大惯性、大迟延的被控对象,设计了一种控制品质更好的改进型广义预测控制,并结合蚁群算法对广义预测控制参数进行优化,结构简单,容易在计算机中实现。仿真结果表明,结合蚁群算法后的改进型广义预测控制明显优于常规PID控制,能够更快、更准确地跟踪设定值,大幅减少调节时间,获得基于常规GPC控制更好的控制效果。In this paper,an improved generalized predictive control with better control quality is designed for the controlled object with large inertia and large delay.Combined with ant colony algorithm,the parameters of generalized predictive control are optimized.The structure is simple and easy to realize in computer.The simulation results show that the improved generalized predictive control combined with ant colony algorithm is obviously better than the conventional PID control,which can track the set value faster and more accurately,greatly reduce the regulation time,and obtain better control effect based on the conventional GPC control.

关 键 词：广义预测控制 常规PID控制 常规GPC控制 蚁群算法 参数优化 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]