# Critically Leveraging Theory for Optimal Control of Quadrotor Unmanned Aircraft Systems
###### Abstract  
In the dynamic realm of Unmanned Aerial Vehicles (UAVs), and, more specifically, Quadrotor drones, this study heralds a ground-breaking integrated optimal control methodology that synergizes a distributed framework, predictive control, H-infinity control techniques, and the incorporation of a Kalman filter for enhanced noise reduction. This cutting-edge strategy is ingeniously formulated to bolster the precision of Quadrotor trajectory tracking and provide a robust countermeasure to disturbances. Our comprehensive engineering of the optimal control system places a premium on the accuracy of orbital navigation while steadfastly ensuring UAV stability and diminishing error margins. The integration of the Kalman filter is pivotal in refining the noise filtration process, thereby significantly enhancing the UAV's performance under uncertain conditions. A meticulous examination has disclosed that, within miniature Quadrotors, intrinsic forces are trivial when set against the formidable influence of control signals, thus allowing for a streamlined system dynamic by judiciously minimizing non-holonomic behaviors without degrading system performance. The proposed control schema, accentuated by the Kalman filter's presence, excels in dynamic efficiency and is ingeniously crafted to rectify any in-flight model discrepancies. Through exhaustive Matlab/Simulink simulations, our findings validate the exceptional efficiency and dependability of the advanced controller. This study advances Quadrotor UAV technology by leaps and bounds, signaling a pivotal evolution for applications that demand high-precision orbital tracking and enhanced noise mitigation through sophisticated nonlinear control mechanisms.  
Quadrotor drones; predictive control; H-infinity control methodologies; Kalman filter; MATLAB/Simulink simulations +
Footnote †: journal: Applied  
Our research enters this evolving landscape with a ground-breaking approach: a novel optimal control system tailored for Quadrotors [13]. This paper unfolds the development of an innovative control methodology, integrating distributed control frameworks with cutting-edge Model Predictive Control (MPC) and H-infinity techniques. At the heart of this approach lies the Kalman filter [14; 15], a critical tool in noise reduction and sensor data refinement, ensuring that the UAV's responses are not just reactive but proactive, able to anticipate and adapt to future states and disturbances.  
In this context, our study not only introduces a novel control mechanism but also addresses the non-holonomic control challenges inherent in Quadrotor dynamics [16; 17; 18; 19; 20; 21]. Through meticulous evaluation and extensive simulations in Matlab/Simulink, we demonstrate that our control system can effectively bypass these constraints without compromising control quality. This development represents a significant leap in UAV technology, marking a new era in high-precision orbital tracking and advanced nonlinear control for noise mitigation. Our work is a testament to the innovative spirit driving the UAV field, signifying a monumental advancement in the practical application of these remarkable machines.