## 3 Methodology
### Genetic Algorithm
Available online: [[https://dl.djicdn.com/downloads/matrice-300/20200507/M300](https://dl.djicdn.com/downloads/matrice-300/20200507/M300) RTKUserManualEN.pdf]([https://dl.djicdn.com/downloads/matrice-300/20200507/M300](https://dl.djicdn.com/downloads/matrice-300/20200507/M300) RTKUserManualEN.pdf) (accessed on 18 April 2022). * [PERSON] (2020) [PERSON] Application of PID Simulation Control Mode in Quadrotor Aircraft. In Proceedings of the 2020 International Conference on Computer Engineering and Application, Guangzhou, China, 18-20 March 2020. * _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] A combined static output feedback-PID control for TITO process based particle swarm optimization: Simulation and practical implementation for the poultry house system. _Int. J. Dyn. Control_**2022**, 1-14. [CrossRef]
* [PERSON] and [PERSON] (2015) [PERSON]; [PERSON] Optimal Control of Twin Rotor MIMO System Using LQR Technique. In _Computational Intelligence in Data Mining_; Springer: New Delhi, India, 2015; Volume 1, pp. 11-21. * [PERSON] (2010) [PERSON]; [PERSON] LQR Controller Design of MIMO Systems with External disturbances based on Stability Degree Constraint. In Proceedings of the 2010 IEEE International Conference on Mechatronics and Automation, Xi'an, China, 4-7 August 2010. * [PERSON] et al. (2007) [PERSON]; [PERSON]; [PERSON] Steering control of six-wheeled vehicles using linear quadratic regulator techniques. _J. Automob. Eng._**2007**, _221_, 1231-1240. [CrossRef]
* _[PERSON] et al. (2015)_ [PERSON]; [PERSON]; [PERSON] Attitude flight control system design of UAV using LQG\LTR multivariable control with noise and disturbance. In Proceedings of the 2015 3 rd RSI International Conference on Robotics and Mechatronics (ICROM), Tehran, Iran, 7-9 October 2015. * [PERSON] and [PERSON] (2015) [PERSON]; [PERSON] [PERSON] LQR state feedback controller based on particle swarm optimization for IPMSM drive system. In Proceedings of the IEEE 10 th Conference on Industrial Electronics and Applications (ICIEA), Auckland, New Zealand, 15-17 June 2015. * [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] The Trade-Off between the Controller Effort and Control Quality on Example of an Electro-Pneumatic Final Control Element. _Actuators_**2019**, \(8\), 23. [CrossRef]
* [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] Optimal Design of Helicopter Control Systems Using Particle Swarm Optimization. In Proceedings of the IEEE International Conference on Industrial Cyber Physical Systems (ICPS), Taipei, Taiwan, 6-9 May 2019. * [PERSON] and [PERSON] (1996) [PERSON]; [PERSON] [PERSON] Advanced helicopter flight control using two-degree-of-freedom H(infinity) optimization. _[PERSON]. Guid. Control. Dyn._**1996**, _19_, 461-468. [CrossRef]
* _[PERSON] et al. (2012)_ [PERSON]; [PERSON]; [PERSON] H-infinity robust control of 3-DOF helicopter. In Proceedings of the International Multi-Conference on Systems, Signals & Devices, Chemnitz, Germany, 20-23 March 2012. * [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] Comparison of Disturbance Rejection Performance between Three Types of UAV Linear Controllers. In Proceedings of the 7 th International Conference on Information Science and Control Engineering (ICISCE), Changsha, China, 18-20 December 2020. * [PERSON] et al. (2021) [PERSON]; [PERSON]; [PERSON]; [PERSON] Quadrotor Stabilization and Tracking Using Nonlinear Surface Sliding Mode Control and Observer. _Appl. Sci._**2021**, _11_, 1417. [CrossRef]
* [PERSON] et al. (2006) [PERSON]; [PERSON] Backstepping Control for a Quadrotor Helicopter. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Beijing, China, 9-15 October 2006. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON]; [PERSON] Control of a Quadrotor with Reinforcement Learning. _IEEE Robot. Autom. Lett._**2017**, \(2\), 2096-2103. [CrossRef]
* [PERSON] et al. (2006) [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-agent quadrotor testbed control design: Integral sliding mode vs. reinforcement learning.