| Popis: |
The possibility of obtaining measuring in-formation about the parameters of the automatic control and monitoring system of a turbofan engine during its operation in flight conditions and almost no restrictions on computational costs enable using algo-rithmic methods to improve the engine fault tolerance. In the previous article, the optimal observer, the Kalman filter, was used as a reserve meter. Its functioning essentially depends on the probabilistic characteristics of the system noise, which is known approximately and changes in a complex man-ner over time. Inaccuracies in these characteristics can lead to filter divergence and loss of its stability. To solve this problem, it is proposed to use the Yazvinsky adaptive filter, which allows calculating the covariance matrix of the disturbance noise after the arrival of new measurements from the values of the updated process of the Kaman filter. In this case computational costs increase insignificantly. The use of the Yazvsky adaptive filter eliminates the need for preliminary correlation analysis of system noise and pro-tects the Kalman filter from loss of stability. The results of modeling the Yazvinsky filter, consistent with the mathematical model of the turbofan engine, based on the flight test data of the PS-90A type engine as part of the TU-214 aircraft, both in steady-state and transient modes of engine operation, are presented. The parameters are indicated as percentage of the maximum values. A comparative analysis of the actual errors in the estimation of the output vector of the turbofan engine ACS when Kal-man and Yazvinsky filters are operating is carried out. The results of the comparative analysis are presented in the form of tables and graphs. It is shown that the proposed algorithm ensures fulfillment of the requirements for the accuracy of estimating the output vector of the automatic control and monitoring system of a gas turbine engine and can be recommended for use in the ACS of the turbofan engine. |
