Abstract
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.
基金项目
国家科技重大专项(J2019-I-0010-0010)
中国博士后科学基金(2021M701692)
中央高校基本科研业务费专项(NS2022029)
Postgraduate Research & Practice Innovation Program of NUAA,China(xcxjh20220206)
国家自然科学基金(51976089)
Jiangsu Funding Program for Excellent Postdoctoral Talent,China(2022ZB202)
NETL
NSTL
万方数据