The Derivative-free nonlinear Kalman Filter is used for developing a robust controller which can be applied to quadcopters. The control problem for quadcopters is nontrivial and becomes further complicated if this robotic system is subjected to model uncertainties and external disturbances. Using differential flatness theory it is shown that the model of a quadcopter can be transformed to linear canonical form. For the linearized equivalent of the quadcopter it is shown that a state feedback controller can be designed. Since certain elements of the state vector of the linearized system can not be measured directly, it is proposed to estimate them with the use of a novel filtering method, the so-called Derivativefree nonlinear Kalman Filter. Moreover, by redesigning the Kalman Filter as a disturbance observer, it is is shown that one can estimate simultaneously external disturbance terms that affect the quadcopter or disturbance terms which are associated with parametric uncertainty. The efficiency of the proposed control scheme is checked through simulation experiments.
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