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

作     者：Ziyu GU Qiuhong LI Shuwei PANG Wenxiang ZHOU Jichang WU Chenyang ZHANG Ziyu GU;Qiuhong LI;Shuwei PANG;Wenxiang ZHOU;Jichang WU;Chenyang ZHANG

作者机构：Jiangsu Province Key Laboratory of Aerospace Power SystemNanjing University of Aeronautics and AstronauticsNanjing 210016China AECC Hunan Aviation Powerplant Research InstituteZhuzhou 412002China 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2024年第37卷第4期

页      面：493-507页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 082502[工学-航空宇航推进理论与工程] 0835[工学-软件工程] 0825[工学-航空宇航科学与技术] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：co-supported by the National Science and Technology Major Project, China (No. J2019-Ⅰ-0010-0010) the Project funded by China Postdoctoral Science Foundation (No. 2021M701692) the Fundamental Research Funds for the Central Universities, China (No. NS2022029) the Postgraduate Research & Practice Innovation Program of NUAA, China (No. xcxjh20220206) the National Natural Science Foundation of China (No. 51976089) Jiangsu Funding Program for Excellent Postdoctoral Talent, China (No. 2022ZB202) 

主　　题：Adaptive control systems Turbo-shaft engine Neural network Nonlinear state space equation NARMA-L2 

摘      要：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) algorithm 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. Simulation results show that, compared with the NARMA-L2 model, the NSSE model of the turboshaft 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.