An online optimization and multivariable control design method for aeroengine based on model prediction

Abstract

An aero-engine online optimization and multi-variable control design method based on model predictive control, which realizes the control and online optimization of multiple aero-engine variables according to requirements such as thrust and rotational speed under satisfying constraints. The control system consists of two parts: the first part is the prediction model acquisition layer, based on the actual working state of each control cycle of the aeroengine and the external environmental parameters, the engine small deviation linear model near different steady-state points is continuously established, and the model parameters Provided to the controller; the second part is the control law decision-making layer, which is a closed-loop structure composed of a model predictive controller and external output feedback. The model predictive controller is based on the engine model in the current state, control instructions and related constraints, by solving The linear optimization problem determines the output of the controller at the next moment, and the external output feedback introduces the actual output of the aeroengine into the decision of the controller for the future control quantity to compensate for the influence of model mismatch and external disturbance.

Description

technical field [0001] The invention provides an aeroengine online optimization and multivariable control design method based on model prediction, which belongs to the technical field of aerospace propulsion system control and simulation. Background technique [0002] With the continuous advancement of aero-engine technology, the structure of modern aero-engines has become increasingly complex, and the scope of work has also continued to expand. The requirements for aero-engine control systems have also become higher and higher. The traditional single-input / single-output control system has been difficult to meet the control requirements. . Therefore, selecting more control variables to realize the multi-variable control system that controls multiple parameters of the engine has become an important means to improve the performance of the engine control system. As a model-based multi-variable control algorithm, model predictive control can not only realize the effective contr...

AI Technical Summary

Problems solved by technology

Although model predictive control has many excellent characteristics, it also has the disadvantage that it is difficult to balance the performance of the controller with the real-time performance of the control system.

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