The Research Of Aeroengine Intelligent Robust Control

Aeroengine are becoming increasingly complex, with more control variables, to meet future demands on performance. To utilize the potential of the engines, it is also necessary to use more advanced control cenceptes such as multivariable, intelligent, robust control. Robust control can get good performance both at design point and off-design point. It has good application perspective at aeroengine control domain. Using the intelligent algorithm to optimize the paremeters of robust controller, can further eliminate the coupling between the control loops and enhance the system performance. The multivariable robust control of aeroengine is the topic of this paper. It includes aeroengine modelling, robust controller parameters optimization, intelligent thrust estimation and control. The four-input controller for aeroengine is designed and tested based on the control structure design at last.The nonlinear component level model of aeroengine is researched in the paper. It provides the simulation platform. The state variable modeling method of aeroengine based on the intelligent optimization is proposed. It can get the linear model without complicated formula deduction and has no limitation on the system modes. The particle swarm optimization and the least square optimization are combined and used to build the aeroengine four-input state variable model. The precision of the model is improved by the combination optimization. In the design of multivariable robust control system, the intelligent parameters optimization method is proposed and used in aeroengine rotor speed control system. It can make the performance request clearly and avoid the shortcoming of performance indices weighted sum method that the weights have no clearly corresponding relationship with the system response. The aeroengine rotor speed control system is designed based on the wevelet network PID control and decoupling ALQR control. The inverse model control method is proposed based on the control structure design and used in aeroengine thrust control system. The neural network, support vector machine and Kalman filter are researched in thrust estimation. The tracking filter thrust estimate method based on controller is proposed. The neural network inverse model and the linear inverse model are adopted to control the thrust combined with the PI control algorithm. The simulation results show that the thrust estimate and control results are satisfied.At last, the control structure design of aeroengine four-input robust control system is researched. The monotony of the input variables to the output variables of the enigne is analysed. The block decoupling control method base on LQ/H∞is proposed and used to design the four-variable engine control system. The method is validated by digital and semi-physical testing.