Abstract:
This report presents the modeling and control of a coaxial octorotor drone, a type of multiro-
tor UAV with enhanced stability and payload capacity. First, the complete nonlinear dynamic
model of the drone is developed, capturing both translational and rotational motions. Based on
this model, several advanced control strategies are designed and implemented to ensure stable
flight and accurate trajectory tracking. These include the classical PID controller, Backstep-
ping, Sliding Mode Control (SMC), Adaptive Direct Control, and Fuzzy Logic Control (FLC).
To optimize the performance of these controllers, their gains and parameters are tuned using
two nature-inspired optimization algorithms: Particle Swarm Optimization (PSO) and the Ge-
netic Algorithm (GA). The comparative analysis demonstrates the strengths and limitations
of each control method in terms of robustness, convergence, and tracking accuracy. Simulation
results validate the effectiveness of the proposed control schemes and highlight the advantage
of intelligent optimization in enhancing UAV performance.
