Research On Multivariable Model Reference Adaptive Control Methods For Turbofan Engines

Taking a turbofan engine as the research object,this paper designs multivariable model reference adaptive control methods with modifications for dynamic performance improvement,robustness enhancement and non-linear compensation,and applies them to the dual variable control system of high pressure rotor speed and engine pressure ratio.Three multivariable model reference adaptive control algorithms of state feedback state tracking,state feedback output tracking and output feedback output tracking are proposed.The complexity of the three algorithms is compared qualitatively and quantitatively,followed by the analysis of merits and applicability.In application to a turbofan engine,with the input characteristics of which analyzed and the invariance of system information required by the three algorithms confirmed,the controller parameters are not dependent on the model.Simulation indicates the three algorithms can both achieve zero-static-error tracking control at different operating points in the engine envelope,but the dynamic performance needs to be further improvedFor the multivariable model reference adaptive control of state feedback state tracking,a model reference adaptive control method of turbofan engines based on the error-feedback reference model is proposed.This method reduces the overshoot of system responses and improves dynamic performance of the system on the basis of asymptotic tracking.In view of the parameter drift phenomenon of model reference adaptive control in the presence of external interference,a multivariable model reference adaptive control method based on e-modified adaptive law is proposed for turbofan engines,with the uniform ultimate boundedness of the control system signals proved.Simulation results illustrate both good dynamic and static performance of the control system at different points of the envelope and robustness to external interference.To deal with problems of large envelope and strong nonlinearity,which linear model reference adaptive control does not cover,a multivariable model reference adaptive control method based on neural network compensation for turbofan engines is proposed.Based on Lyapunov stability theory,an online regulation law of network weight is designed,assuring uniform ultimate boundedness of all signals in the system.Simulation demonstrates both good dynamic and static performance of the multivariable model reference adaptive control compensated by RBF network in the envelope.RBF network effectively compensates the nonlinear characteristics caused by the change of flight conditions.