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Turbo-shaft engine adaptive neural network control based on nonlinear state space equation

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Intelligent Adaptive Control(AC)has remarkable advantages in the control system design of aero-engine which has strong nonlinearity and uncertainty.Inspired by the Nonlinear Autoregressive Moving Average(NARMA)-L2 adaptive control,a novel Nonlinear State Space Equation(NSSE)based Adaptive neural network Control(NSSE-AC)method is proposed for the turbo-shaft engine control system design.The proposed NSSE model is derived from a special neural network with an extra layer,and the rotor speed of the gas turbine is taken as the main state variable which makes the NSSE model be able to capture the system dynamic better than the NARMA-L2 model.A hybrid Recursive Least-Square and Levenberg-Marquardt(RLS-LM)algo-rithm is advanced to perform the online learning of the neural network,which further enhances both the accuracy of the NSSE model and the performance of the adaptive controller.The feedback correction is also utilized in the NSSE-AC system to eliminate the steady-state tracking error.Sim-ulation results show that,compared with the NARMA-L2 model,the NSSE model of the turbo-shaft engine is more accurate.The maximum modeling error is decreased from 5.92％to 0.97％when the LM algorithm is introduced to optimize the neural network parameters.The NSSE-AC method can not only achieve a better main control loop performance than the traditional controller but also limit all the constraint parameters efficiently with quick and accurate switching responses even if component degradation exists.Thus,the effectiveness of the NSSE-AC method is validated.

Adaptive control systemsTurbo-shaft engineNeural networkNonlinear state space equa-tionNARMA-L2

Ziyu GU、Qiuhong LI、Shuwei PANG、Wenxiang ZHOU、Jichang WU、Chenyang ZHANG

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Jiangsu Province Key Laboratory of Aerospace Power System,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China

AECC Hunan Aviation Powerplant Research Institute,Zhuzhou 412002,China

国家科技重大专项中国博士后科学基金中央高校基本科研业务费专项Postgraduate Research & Practice Innovation Program of NUAA,China国家自然科学基金Jiangsu Funding Program for Excellent Postdoctoral Talent,China

J2019-I-0010-00102021M701692NS2022029xcxjh20220206519760892022ZB202

2024

10.1016/j.cja.2023.08.012

中国航空学报(英文版)
中国航空学会

中国航空学报(英文版)

CSTPCDEI
影响因子：0.847
ISSN：1000-9361
年,卷(期)：2024.37(4)
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