摘要
针对增程式辅助动力单元(APU)工作点切换过程的控制,提出了一种混沌退火混合粒子群(CAHPSO)算法优化模糊比例积分微分(PID)控制的增程式APU动态控制策略。该算法将标准粒子群(PSO)算法与混沌搜索和退火机制融合,强化全局寻优能力,并采用该算法离线优化模糊PID控制参数。为验证该控制策略的有效性,本文建立了APU仿真模型。仿真结果表明:该控制策略可使APU在工作点从热机点逐步切换至高负荷点的过程中稳定时间短,在三个工作点切换控制过程中稳定时间分别为2.92 s,2.88 s,2.79 s;可使APU转速超调率小,在小负荷点向中负荷点切换时超调约0.95%,在其余过程未出现超调;可使APU转矩变化平缓,在中负荷点向高负荷点切换时转矩仅超调0.16 N·m,具有良好的动态控制效果。
For the control of the working point switching process of the extended-range auxiliary power unit(APU),a dynamic control strategy for the extended-range APU was proposed by using the chaotic annealing hybrid particle swarm optimization(CAHPSO)algorithm to optimize the fuzzy proportional-integral-derivative(PID)control.This algorithm was used to combine chaos search and annealing mechanisms based on the standard particle swarm optimization(PSO)to enhance the global optimization ability,and optimize the fuzzy PID control parameters offline.In order to verify the effectiveness of the new control strategy,the APU system simulation model was established.The simulation results showed that during the processes of gradually switching from the warming-up point to the high load point,the new control strategy can make the APU shorten the stabilization time,the stabilization time for the three switching control processes of the working points was 2.92 s,2.88 s,2.79 s,respectively;the new control strategy can make the APU reduce the speed overshoot rate,only when in the switching from the small load point to the middle load point,the speed overshoot rate was about 0.95%and there was no overshoot in other switching processes;the new control strategy can make the APU torque change smoothly,and the torque overshoot was only 0.16 N·m when switching from the middle load point to the high load point,which achieved a good dynamic control effect.
作者
赵家豪
魏民祥
丁玉章
常诚
ZHAO Jiahao;WEI Minxiang;DING Yuzhang;CHANG Cheng(College of Energy and Power Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China)
出处
《航空动力学报》
EI
CAS
CSCD
北大核心
2021年第6期1213-1221,共9页
Journal of Aerospace Power
基金
国防预研项目(513250203)。
