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The NAVKA navigation technologies for distributed GNSS/MEMS/MOEMS sensors, developed since 2008 at IAF/HSKA and have been awarded “Bronze” at the European Satellite Navigation Competition (ESNC) 2014. The NAVKA flight-control system (algorithms, software and hardware) is one part of the above technologies. It can be used for the navigation and control of UAV and manned multicopters. Hereby any n-propeller multicopter can be designed scalable in respect to the 3D propeller layout and orientation in the body (b) frame, the applications, the size and the payload, as well as the further system equipment in case of UAV. The NAVKA flight navigation and control system is based on at least one, generally a redundant number of m GNSS/MEMS and camera (MOEMS) multisensor platforms (p) situated on the body (b). At first the NAVKA multisensor-multiplatform concept for the estimation of the navigation state vector y(t) of distributed GNSS/MEMS/MOEMS sensor data is presented. A second focus is then put on the flight physics and the physical and mathematical background of the control of an n-propeller multicopter. The general relations between the above 15-parameter navigation state vector y(t) and its changes and the n-dimensional control state vector u(t), given by the n propeller rotation rates, is derived. Based on the above general physical background and equations of the flightdynamics of an n-propeller body, the estimation of the flight control vector u(t) an its relation to a PID-controller are presented. At first the navigation state y(t) of one, or a number of m distributed navigation boxes, are regarded as one control unit for the estimation of u(t). The respective NAVKA PID-controller concept is derived, and the developed flight-control system (hard- and software) NAVKArine-FC4 is presented. In order to receive a secure flight control system, the redundancy of the above mentioned m physically separated navigation boxes and their navigation state y(t)ᵢ and control state estimates u(t)ᵢ, respectively can be used to provide in realtime a final robust M-estimate of u(t). Like for the estimation of the navigation state estimation y(t) from sensor data, a robust L₁-norm (instead of least squares) leads to a secure control state estimate u(t) in case of eventual errors in the single estimation u(t)ᵢ used as input. Based on SIMPLEX-algorithms (instead of iterative least squares), the L₁-norm further allows to consider different kind of restrictions, e. g. in terms of in-equations on the control state vector u(t) of the propeller rotation rates.