![]() The results show that the proposed method provides stability improvement, better transient response, and power consumption. Finally, the performance comparison between the proposed and conventional methods is presented. A genetic algorithm (GA) will be used to tune the MS-PID controller. ![]() Once the model is defined, a robust multi-stage PID controller for the non-inertial referenced frame symmetrical multirotor UAV is designed, tuned, and tested. The work presents a model for a non-referenced inertial frame multirotor UAV (quadcopter). This study intends to investigate the design and development of a highly tuned robust multi-stage PID controller for a symmetrical multirotor UAV. Furthermore, the proposed AGA generates new chromosomes for "new solutions" by randomly developing new solutions close to the previous best values, which will prevent any local minima solution. The MS-PID parameters are optimized in parallel, as every PID controller affects the other controller's behavior and performance. The proposed method optimizes the offline-planned approach, providing several possibilities for adapting the controllers with various paths and or varying weather conditions. An adaptive genetic algorithm (AGA) is utilized to optimize the MS-PID controllers for controlling the quadrotor in this study. This is due to the number of variables and hyperparameters tuned during the process. An accurate tuning process of such a controller depends on the engineer's experience level. The designs were always concerned with the enhanced response, robustness, model reduction and performance of PID controllers. Several researchers have investigated the structure and design of PID controllers for high-order systems during the last decade. Symmetrical multirotor UAVs are unstable systems, and it is thought that the kinematics of the symmetrical UAV rotor, such as the quadrotor and hexacopter resembles the kinematics of an inverted pendulum. The design and implementation of a multi-stage PID (MS-PID) controller for non-inertial referenced UAVs are highly complex. Future payloads such as a medical kit and detachable communications links are considered for the next phase. The tests were conducted in remote and actual environments given the three different landing gears, and the results show that the drone is ready for a real mission. It can land and take off in ground and surface water and maneuver in different environments air, ground (flat area), and surface water, depending on the attached payload. It can communicate through a remote controller via radio frequency signals. weight and can support payload weighing 5 kg., it was tested to operate at a maximum altitude of 100 m. The drone has an average flight time of 15 minutes, 4 kg. A mapper payload that performs image stitching is also presented. This method allowed the drone to move in multi-mission or other environments (ground, air, and surface water) in normal conditions by changing its landing gears. These also include the design of different landing gears that are mechanically replaceable depending on the mission. In this report, the authors designed an unmanned vehicle composed of a flight mechanism & power supply, frame, and different payloads for different missions. Drones, or Unmanned Aerial Vehicles (UAVs), are already being utilized to help people in need worldwide.
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