Unmanned aerial vehicles like many robotic systems are complex in nature. Design and testing of such systems can take enormous amount of time and resources. Model based design techniques can be used to divide the interrelated behavior of each subsystem, as a prelude for thoroughly testing and validating the designed algorithms and controllers. In this context, integrated flight control subsystems for mini unmanned aerial vehicles (MAVs) were rapidly developed using model based design tools. The six-degrees of freedom, nonlinear mathematical model was employed to simulate the MAV dynamics. Tornado vortex lattice method (VLM) was used to numerically estimate the aerodynamic and control derivatives. Estimation of sensors noise parameters from different calibration tests enabled a realistic modeling of flight sensors. Optimal extended Kaiman filter (EKF) estimator was designed and integrated in the simulation to estimate immeasurable attitude states. Moreover, EKF attitude estimator was tested offline and compared with direction cosine matrix attitude estimation algorithm for real flight data. Straight line and orbit following algorithms were combined to allow MAV to autonomously navigate and loiter through a desired waypoint path. Transition management algorithm is proposed to obey loiter commands at any waypoint, on or off the path. Finally, the flight control system was validated using processor in the loop (PIL) simulation, where the digital implementation of the control system was thoroughly tested on low cost Arduino embedded microcontroller. Automatic embedded-code generation tool was used to target Arduino microcontroller for rapid and optimized code implementation, in addition to facilitating host-target serial communication for PIL simulation. Finally, model in the loop (MIL) response of the flight controller was compared and verified versus PIL simulation counterpart. In conclusion, model-based design tools and PIL validation technique were proved to be effective in rapidly testing cutting edge flight control algorithms, while minimizing the time, effort and cost of testing on actual air vehicles.
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