Kalman state estimator for aircraft structural load alleviation using eigenpair assignment

Process of finding the “best estimate” from noisy signals amounts to “filtering out” the noise. Many methods exist to filter the unwanted noise. A tried and tested method is to use a Kalman Filter which not only cleans up the signals but can also be used to provide signal estimates, for use in reduced order feedback control. Typically, Kalman filter gains are computed using the Linear Quadratic Regulator theory. Reduced order feedback control has been applied in aircraft control problems. One such application area is in synthetic load alleviation, structural loads that arise due to gusts and or control surface deflections. Aircraft structural load alleviation necessitates the use of robust feedback control. The controllers are required to provide load alleviation in cases where the feedback signals are contaminated with noise or missing due to sensor failures. In this paper a method of computing the Kalman gains for the purposes of reduced order feedback is presented, which completely specifies the error dynamics of the estimator in terms of its eigenvalues and associated eigenvectors. The state estimator synthesized using the proposed approach has excellent noise rejection properties and shown to be robust and can be successfully used in reduced order state feedback control, specifically in Aircraft Structural Load Alleviation control schemes. The proposed scheme demonstrates reduction in structural loads.