Abstract: The present invention belongs to the technical field of engine modeling, and provides a methodology for modeling a starting process of a micro gas turbine engine, comprising the following steps: modeling micro gas turbine engine speed; modeling a relationship between micro gas turbine engine performance parameters and speed; and performing error analysis. Since most existing micro gas turbine engine modeling methodologies are methodologies using pure mechanism or machine learning, it is difficult to accurately describe the starting process of a micro gas turbine engine, and machine learning requires a lot of test data. In engineering practice, pure mechanisms commonly used at present are complicated in operation, low in efficiency, and low in modeling accuracy. The invention provides a methodology for modeling a starting process of a micro gas turbine engine based on a combination of a mechanism and an identification method, which makes up for the deficiencies of the prior art.

Abstract: A design method of aero-engine on-line optimization and multivariable control based on model prediction control realizes aero-engine multivariable control and on-line optimization according to thrust, rotational speed and other needs under the condition of meeting constraints. The first part is a prediction model acquisition layer that continuously establishes a small deviation linear model of an aero-engine near different steady state points based on the actual operating state of the aero-engine in each control cycle and external environment parameters and that supplies model parameters to a controller; and the second part is a control law decision-making layer which is a closed loop structure that consists of a model prediction controller and an external output feedback. The model prediction controller determines the output of the controller at next moment by solving a linear optimization problem according to an engine model in the current state, a control instruction and relevant constraints.