Affiliation(s):
School of Electrical Engineering, Guizhou University, Guiyang 550025, China;
moreAffiliation(s): School of Electrical Engineering, Guizhou University, Guiyang 550025, China; School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China;
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Kailong MOU, Mengjian ZHANG, Deguang WANG, Ming YANG, Chengbin LIANG. Enhanced hippopotamus optimization algorithm for tuningPIDcontrollers[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2400492
@article{title="Enhanced hippopotamus optimization algorithm for tuningPIDcontrollers", author="Kailong MOU, Mengjian ZHANG, Deguang WANG, Ming YANG, Chengbin LIANG", journal="Frontiers of Information Technology & Electronic Engineering", year="in press", publisher="Zhejiang University Press & Springer", doi="https://doi.org/10.1631/FITEE.2400492" }
%0 Journal Article %T Enhanced hippopotamus optimization algorithm for tuningPIDcontrollers %A Kailong MOU %A Mengjian ZHANG %A Deguang WANG %A Ming YANG %A Chengbin LIANG %J Frontiers of Information Technology & Electronic Engineering %P %@ 2095-9184 %D in press %I Zhejiang University Press & Springer doi="https://doi.org/10.1631/FITEE.2400492"
TY - JOUR T1 - Enhanced hippopotamus optimization algorithm for tuningPIDcontrollers A1 - Kailong MOU A1 - Mengjian ZHANG A1 - Deguang WANG A1 - Ming YANG A1 - Chengbin LIANG J0 - Frontiers of Information Technology & Electronic Engineering SP - EP - %@ 2095-9184 Y1 - in press PB - Zhejiang University Press & Springer ER - doi="https://doi.org/10.1631/FITEE.2400492"
Abstract: Effectively tuning the parameters of proportional-integral-derivative (PID) controllers has persistently posed a challenge in control engineering. This study proposes enhanced hippopotamus optimization (EHO) to address this challenge. Latin hypercube sampling and adaptive lens reverse learning are used to initialize the population to improve population diversity and enhance global search. Additionally, an adaptive perturbation mechanism is introduced into the position update in the exploration phase. To validate the performance of EHO, it is benchmarked against hippopotamus optimization and four classical or state-of-the-art intelligent algorithms using the CEC2022 test suite. The effectiveness of EHO is further evaluated by applying it in tuning PID controllers for different types of systems. The performance of EHO is compared with five other algorithms and the classical Ziegler-Nichols method. Analysis of convergence curves, step responses, box plots, and radar charts indicates that EHO outperforms the comparison methods in accuracy, convergence speed, and stability. Finally, EHO is used to tune the cascade PID controller for trajectory tracking in a quadrotor unmanned aerial vehicle to assess its applicability. The simulation results indicate that the integral of the time absolute error for the position channels (x, y, z) when the system is optimized using EHO over an 80s runtime are 59.979, 22.162, and 0.017, respectively. These values are notably lower than those obtained by the original hippopotamus optimization and manual parameter adjustment.
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