Design Of Multi-variable Optimal Control System For Variable Cycle Engine

Turbine temperature is one of the key performance parameters of Variable Cycle Engine(VCE),which can reflect the service life of the engine.Through the optimization of geometrically adjustable components,on the premise of maintaining the same engine thrust and meeting various safe working constraints,the temperature after the engine turbine can be reduced to achieve the goal of extending the service life of the VCE,that is,the lowest turbine temperature mode.At the same time,there is a strong coupling relationship between the parameters of multiple adjustable components when the VCE is switched from the normal cruise mode to the minimum turbine temperature mode.Relying on the "XX Engine Fundamental Problem Research" project,a multivariable optimal control system design research is carried out based on a certain type of variable cycle engine.The main research contents are as follows:Aiming at the constrained VCE minimum turbine temperature optimization problem,this paper proposes to incorporate the two-norm of the thrust difference into the optimization evaluation function,and use the penalty function method to process the output constraints,so as to convert the constrained optimization problem into an unconstrained optimization problem.Specifically,the grey wolf optimization algorithm is used under the conditions of the ground conditions and the intermediate state of the engine(the maximum state without afterburner)at different Mach number operating points to simulate and compare it with the sequential quadratic programming algorithm for solving traditional nonlinear optimization problems.optimization effect.The results show that the sequential quadratic programming algorithm has a fast optimization convergence speed,but it is easy to fall into local convergence.When the VCE is not degraded and degraded,the turbine outlet temperature optimized by SQP decreases by 2.19% and 1.54% respectively;while the grey wolf algorithm has better search globality,and the turbine outlet temperature optimized by GWO decreases by 6.16% and 7.08%respectively.Aiming at the strong nonlinearity and multi-parameter coupling problem of VCE,a steadystate multi-variable closed-loop controller based on active disturbance rejection strategy is designed.Firstly,according to the mechanism analysis and sensitivity analysis results of the key components of the variable cycle engine,four pairs of appropriate control variables and controlled variables are selected.Then,considering the difficulty in tuning multiple control parameters of the ADRC strategy caused by the high index requirements of the dynamic and steady-state performance of the VCE switching control process,a block-wise intelligent optimization method for the controller parameters is proposed,that is,different block parameters are set with different performance evaluation index function.Finally,in four typical working conditions,the control effect that the controller can achieve the performance indicators is verified by digital simulation,and the real-time performance and effectiveness of the designed controller are verified by hardware-in-the-loop simulation,which has certain engineering application value.Aiming at the complexity problem caused by the traditional scheduling method of control parameters in the wide envelope,an intelligent multi-variable control method of engine based on genetic algorithm optimization of BP neural network is proposed.This method uses GA to optimize the initial weights and thresholds of the BP neural network to prevent the network from falling into the local optimal solution during the training process;then uses the trained network to design a VCE multivariable closed-loop controller,and selects the non-training condition a within the envelope and a certain working condition b outside the envelope to verify the control effect and generalization ability of GA-BP neural network.Then the control effect of GA-BP is compared with that of the active disturbance rejection strategy.The simulation results show that the method has a good control effect on the typical working conditions in the set envelope,and has a certain generalization ability without dividing the envelope.