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Abstract-This paper presents an LQR-Based 6DOF control of an unmanned aerial vehicles (UAV), namely a small-scale quadrocopter. Due to its high nonlinearity and a high degree of coupling system, the control of an UAV is very challenging. quadrocopter trajectory tracking in a 3D space is greatly affected by the quadrocopter balancing around its roll-pitch-yaw frame. Lack of precise tracking control about the body frame may result in inaccurate localization with respect to a fixed frame. Thus, the present paper provides a high dynamic control tracking balancing system response. An integral LQR-based controller is proposed to enhance the dynamic system response balancing on roll, pitch and yaw. The control on the hovering angles consists of two-cascaded loops. Namely, an inner loop for the angular speed control of each angular motion around the body frame axes, and an outer loop for the desired position control. In general, the proposed balancing control system on roll, pitch and yaw, has six control loops. The proposed control approach is implemented utilizing an embedded ATMega2560 microcontroller system. Practical results obtained from the proposed control approach exhibits fast and robust control response and high disturbance rejection.

Recent development of different control systems for UAVs has caught the attention of academic and industry, due to the wide range of their applications such as in surveillance, delivery, work assistant, and photography. In addition, arms, grippers, or tethers could be installed to UAVs so that they can assist in constructing, transporting, and carrying payloads. In this book chapter, the control laws of the attitude and position of a quadcopter UAV have been derived basically utilizing three methods including backstepping, sliding mode control, and feedback linearization incorporated with LQI optimal controller. The main contribution of this book chapter would be concluded in the strategy of deriving the control laws of the translational positions of a quadcopter UAV. The control laws for trajectory tracking using the proposed strategies have been validated by simulation using MATLAB ® /Simulink and experimental results obtained from a quadcopter test bench. Simulation results show a comparison between the performances of each of the proposed techniques depending on the nonlinear model of the quadcopter system under investigation; the trajectory tracking has been achieved properly for different types of trajectories, i.e., spiral trajectory, in the presence of unknown disturbances. Moreover, the practical results coincided with the results of the simulation results.

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