TY - JOUR
T1 - 航空发动机分数阶PID控制器的参数自整定方法
AU - Li, Yongge
AU - Zhang, Xiao
AU - Xu, Yong
N1 - Publisher Copyright:
© 2023 Journal of Dynamics and Control. All rights reserved.
PY - 2023
Y1 - 2023
N2 - As the key part of the aero engine, the controller is the core component to ensure the normal operation of the engine. With the development of aero engine, it requires higher and higher accuracy and timeliness of the control of aero engine, which promotes to increase the effectiveness of PID controller. In this work, two online and offline parameter self-tuning methods for fractional order PID are proposed to control the thrust of aero engine. Firstly, a fractional PID model is established based on the Caputo fractional calculus definition.Secondly, by introducing the lognormal distribution, an improved cuckoo optimization algorithm is provided to achieve offline parameter tuning.Then.combining with RBF network, the parameter online setting problem for fractional order PID is solved. Finally, the results show that the improved cuckoo optimization algorithm exhibits high performance on offline parameters tuning of fractional order PID. The online parameter tuning based on RBF neural network also works stably. We find that the control effect of fractional order PID is much better than the traditional PID, which greatly improves the thrust control effectiveness.
AB - As the key part of the aero engine, the controller is the core component to ensure the normal operation of the engine. With the development of aero engine, it requires higher and higher accuracy and timeliness of the control of aero engine, which promotes to increase the effectiveness of PID controller. In this work, two online and offline parameter self-tuning methods for fractional order PID are proposed to control the thrust of aero engine. Firstly, a fractional PID model is established based on the Caputo fractional calculus definition.Secondly, by introducing the lognormal distribution, an improved cuckoo optimization algorithm is provided to achieve offline parameter tuning.Then.combining with RBF network, the parameter online setting problem for fractional order PID is solved. Finally, the results show that the improved cuckoo optimization algorithm exhibits high performance on offline parameters tuning of fractional order PID. The online parameter tuning based on RBF neural network also works stably. We find that the control effect of fractional order PID is much better than the traditional PID, which greatly improves the thrust control effectiveness.
KW - RBF network
KW - fractional order PID
KW - intelligent optimization algorithm
UR - http://www.scopus.com/inward/record.url?scp=85179110719&partnerID=8YFLogxK
U2 - 10.6052/1672-6553-2023-011
DO - 10.6052/1672-6553-2023-011
M3 - 文章
AN - SCOPUS:85179110719
SN - 1672-6553
VL - 21
SP - 77
EP - 88
JO - Journal of Dynamics and Control
JF - Journal of Dynamics and Control
IS - 7
ER -