基于改进天鹰算法的隐式广义预测控制  

Implicit generalized predictive control based on improved Aquila optimization algorithm

作　　者：刘倩 陶文华[1] 王智聪 季昭宇 Liu Qian;Tao Wenhua;Wang Zhicong;Ji Zhaoyu(School of Information and Control Engineering,Liaoning Petrochemical University,Fushun 113001,China)

机构地区：[1]辽宁石油化工大学电子信息与控制工程学院,抚顺113001

出　　处：《国外电子测量技术》2023年第3期52-58,共7页Foreign Electronic Measurement Technology

摘　　要：在面对使用不确定的参数控制时隐式广义预测算法响应速度慢,导致辨识精度低下的问题,提出一种基于改进天鹰算法(AO)优化的隐式广义预测控制。首先在天鹰算法4个搜索阶段设置可变的惯性权,使得天鹰算法各个搜索阶段更加均衡,避免了收敛过程耗时长且易陷入局部最优的问题。其次采用改进的天鹰优化算法求出隐式广义预测控制有约束条件时的最优控制。最后将算法应用到某厂循环流化床锅炉进行仿真,从时间上来看,用改进AO算法优化的隐式广义预测的平均仿真速度为3.9831 s,比隐式广义预测平均用的5.9531 s有了明显的速度上的提高,并且在小误差内,能有良好的跟踪效果。仿真结果表明了该算法的可行性,以及其优越的控制性能。To improve the accuracy and responsiveness of implicit generalized prediction control algorithm(IGPC),this paper proposes an implicit generalized prediction algorithm that improves the optimization of the aquila optimizer(AO)algorithm.Firstly,inertial weights are added to the four search stages of the Aquila algorithm to balance the search capabilities of each stage.At the same time,it avoids the problem of local optimality.Then add the Aquila algorithm to the rolling optimization part of the implicit generalized prediction.Gradient optimization is used to find€€the optimal control input under unconstrained conditions.Then,when there are constraints,the implicit generalized prediction improved by Aquila is used for optimization.Finally,the method is applied to the circulating fluidized bed boiler(CFBB)for verification.From the perspective of time,the average simulation speed of implicit generalized prediction optimized by the improved AO algorithm is 3.9831s,which is significantly faster than the average time of implicit generalized prediction of 5.9531s,and it can have a good tracking effect within a small error.The simulation results show the feasibility of the algorithm and its superior control performance.

关 键 词：隐式广义预测 改进天鹰算法 混合优化 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]