Digital adaptive flight controller development

A design study of adaptive control logic suitable for implementation in modern airborne digital flight computers was conducted. Two designs are described for an example aircraft. Each of these designs uses a weighted least squares procedure to identify parameters defining the dynamics of the aircraft. The two designs differ in the way in which control law parameters are determined. One uses the solution of an optimal linear regulator problem to determine these parameters while the other uses a procedure called single stage optimization. Extensive simulation results and analysis leading to the designs are presented

Modern methods for power system harmonics estimation

Harmonics has been present for a long time and its presence shapes the performance of a power system. Therefore, estimation of harmonics is of paramount importance while analyzing a power system network. Following the inception of harmonics, various filters have been devised to achieve an optimal control strategy for harmonic alleviation. This thesis introduces various algorithms to analyze harmonics in the power system. The objective is to estimate the power system voltage magnitude in the presence distortions taking into account the noise by employing different estimation approaches. We have focused our attention towards the study of Least Mean Squares (LMS) based filter, Recursive Least squares (RLS) based filter, Kalman filter (KF) and Extended Kalman (EKF) filter. For a test signal LMS, RLS, KF and EKF based algorithms have been analyzed and results have been compared. The proposed estimation approaches are tested for only static signals

Recursive search-based identification algorithms for the exponential autoregressive time series model with coloured noise

This study focuses on the recursive parameter estimation problems for the non-linear exponential autoregressive model with moving average noise (the ExpARMA model for short). By means of the gradient search, an extended stochastic gradient (ESG) algorithm is derived. Considering the difficulty of determining the step-size in the ESG algorithm, a numerical approach is proposed to obtain the optimal step-size. In order to improve the parameter estimation accuracy, the authors employ the multi-innovation identification theory to develop a multi-innovation ESG (MI-ESG) algorithm for the ExpARMA model. Introducing a forgetting factor into the MI-ESG algorithm, the parameter estimation accuracy can be further improved. With an appropriate innovation length and forgetting factor, the variant of the MI-ESG algorithm is effective to identify all the unknown parameters of the ExpARMA model. A simulation example is provided to test the proposed algorithms