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An adaptive neural observer design is presented for the nonlinear quadrotor unmanned aerial vehicle (UAV). This proposed observer design is motivated by the practical quadrotor where the whole dynamical model of system is unavailable. In this paper, dynamics of the quadrotor UAV system and its state space model are discussed and a neural observer design, using a back propagation algorithm is presented. The steady state error is reduced with the neural network term in the estimator design and the transient performance of the system is improved. This proposed methodology reduces the number of sensors and weight of the quadrotor which results in the decrease of manufacturing cost. A Lyapunov-based stability analysis is utilized to prove the convergence of error to the neighborhood of zero. The performance and capabilities of the design procedure are demonstrated by the Simulation results.

For the operation of autonomous microgrid (MG), an essential task is to meet the load demand sharing using multiple distributed generation (DG) units. The conventional droop control methods and its numerous variations have been developed in the literature in order to realize proportional power sharing amongst such multiple DG units. However, the conventional droop control strategies are subjected to power sharing error because of non-trivial feeder impedances of medium-voltage MGs. Further, complex MGs configurations (mesh or looped networks) usually make to reactive power sharing and system voltage regulation more challenging. This paper presents an optimal control strategy in order to perform the proportional power sharing and voltage regulation for multiple feeders in islanded AC MGs. The case study simulation for optimizing the power sharing and voltage regulations in proposed control strategy has been verified through using MATLAB/Simulink systems.