Large-Signal Estimation for Stochastic Nonlinear Multivariable Dynamic Systems.

摘要

Estimation algorithms for large-signal transient operation of nonlinear multivariable dynamic systems were developed and evaluated. Kalman methodology was employed in defining the filtering logic. Three estimation algorithms were defined based on representing the nonlinear system model by a reduced-order linear model,piecewise-linear model,and nonlinear model. Model-mismatch compensation techniques were established to account for the mismatch between the models in the filtering algorithms and the actual nonlinear system model. Filter gains for each algorithm were calculated off-line using modern state-space estimation techniques. An important constraint on the estimation algorithms is that their computational requirements be compatible with projected airborne digital computer capabilities. The estimation algorithms were evaluated and compared by application to noise-corrupted measurement data generated by a nonlinear digital dynamic F100/F401engine simulation. Estimation of unmeasurable as well as measureable engine variables throughout the idle to military sea-level static operating regime (9to 100percent thrust) for large-signal transients was investigated. Measurable variables are available only through noisy sensors which contain inherent lags. On the basis of these noise-corrupted measurements,the filtering logic generates estimates of measurable and unmeasurable variables that are critical to satisfactory engine operation. Estimation of key engine variables from nominal-engine data,degraded-engine data and engine data with off-nominal noise statistics was evaluated.