航空发动机状态变量模型的QPSO寻优混合求解法

State space modeling based on QPSO hybrid method for aero-engines

关键词:  航空发动机  状态变量模型  量子粒子群优化算法  阶跃响应法

Key words:  Aero-engine  State variable model  QPSO  Steady state response method

摘要:

针对航空发动机控制和故障诊断中的状态变量模型求解存在的系数矩阵精度不高的问题,结合阶跃响应法和拟合法的基础上,提出了一种基于量子粒子群寻优(QPSO)求取发动机状态变量模型的混合求解法。QPSO优化算法求解A,C矩阵使得状态变量模型和非线性模型在动态过程具有较好的吻合,阶跃响应法求取B,D矩阵保证了模型稳态响应一致。利用混合求解法建立了某型涡轴发动机在某一稳态工作点下的小偏离状态变量模型。仿真结果表明,这种方法不仅增强了状态变量模型的求解精度,相对于单纯的拟合法缩短了求解时间,精确的状态变量模型为进一步的故障诊断和控制系统设计提供了条件。

Abstract:

In order to acquire the on-board self-tuning model of aero-engine for gas-path analysis,a hybrid method based on the quantum particle swarm optimization(QPSO) is proposed.The method is composed with QPSO and steady state response method,where A and C matrices are obtained by the QPSO to make the state space model outputs fit with nonlinear model outputs in the dynamic procedure,B and D matrices are obtained by the steady state response method to make their outputs fit in the steady procedure.The state space model of a turbo-shaft engine has been established by the proposed method.Simulation results show that this method can not only improve the accuracy of the state space model,but also reduce the time cost compared with least-squares approximation method.The accurate state space model provides precondition of engine fault diagnostics and control system design.