【摘要】 对涡轴发动机进行了非线性模型预测控制(NMPC)研究,设计了非线性模型预测,该控制器主要包括3个方面:预测模型、滚动优化和反馈校正。利用神经元网络模型预测涡轴发动机动态响应过程,得到预测模型;运用序列二次规划(SQP)优化算法进行发动机的在线滚动优化,得到发动机的燃油控制量;根据神经元网络模型与实际发动机对象的输出误差,对控制器的指令信号进行了反馈校正。最后进行了仿真实验,与常规串级PID控制相比较,非线性模型预测控制器的超调量从2.2%降低到0.8%,响应时间从6 s降低到2 s,具有很好的控制品质。更多还原

【Abstract】 A controller of a turboshaft engine is designed by the nonlinear model predictive control(NMPC) method.The model predictive control system is mainly comprised of three parts: a predictive model,its online optimization and feedback correction.A neural network model is used to predict the dynamic response of the turboshaft engine and generate the predictive model of the NMPC;the sequential quadratic programming(SQP) optimization algorithm is used to optimize the fuel flow of the turboshaft engine online;the error between the neural network model output and the real engine output is used to correct by feedback,the reference of the controller.Finally,a digital simulation is performed,which shows that compared with the serial PID controller the overshoot of the NMPC controller is reduced from 2.2% to 0.8%,and the response time is reduced from 6 s to 2 s.So the NMPC controller has better control quality.更多还原