Problems in signal processing and medical imaging often lead to calculating sparse solutions to under-determined linear systems. Methodologies for solving this problem are presented as background to the method used in this work where the problem is reformulated as an unconstrained convex optimization problem. The least squares approach is modified by an l1-regularization term. A sparse solution...

A multirotor is a type of aerial vehicle that has attracted a lot of attention in recent years. Multirotors have found applications in a variety of different fields and they are used by scientists and researchers, commercial UAV companies and radio control enthusiasts alike. In this thesis a multirotor with eight rotors, also called an octorotor, is used. A physical model of the octorotor has b...

The main focus of this article is to investigate the effects of Adaptive control on the switching frequency and Total Harmonic Distortion of a modified, minimally switched hysteresis controlled shunt active power filter. Reference current generation was done using Instantaneous real and reactive power algorithm. Pulses to the minimally switched grid interactive inverter in which only two switch...

This paper proposes distributed adaptive algorithms based on the conjugate gradient (CG) method and the diffusion strategy for parameter estimation over sensor networks. We present sparsity-aware conventional and modified distributed CG algorithms using l1 and log-sum penalty functions. The proposed sparsity-aware diffusion distributed CG algorithms have an improved performance in terms of mean...

NASA’s Generic Transport Model (GTM) is a remote-controlled, 5.5 percent scale commercial aircraft. An L1 adaptive controller was recently designed and flight tested on the GTM. Oscillations in the elevator command were observed at 1.4 to 2 Hertz during the first flight test in September 2009. In most flight conditions the L1 adaptive controller can be approximated by a linear time-invariant sy...

an adaptive input-output linearization method for general nonlinear systems is developed without using states of the system. another key feature of this structure is the fact that, it does not need model of the system. in this scheme, neurolinearizer has few weights, so it is practical in adaptive situations.  online training of neurolinearizer is compared to model predictive recurrent training...