Application of Input and State Multiple Model Adaptive Estimator for Aircraft Airspeed Approximation

This paper addresses the problem of state estimation in the presence of unknown inputs if the system is dependent on an uncertain parameter. Multiple Model Adaptive Estimation is applied to state and unknown input observers. The theory is evaluated on an aircraft airspeed estimator algorithm. The developed algorithm is based-on linear time invariant (LTI) models of an aircraft linearized at distinct airspeed values. Separate Kalman Filters are designed for aircraft LTI models, run parallel, and the airspeed estimate is the weighted sum of the airspeed estimates provided by the Kalman Filters. Linear and nonlinear test simulations without and with measurement noise proved that the developed algorithm is able to provide accurate airspeed estimates.