Modeling and Optimal Control for Rotary Unmanned Aerial Vehicles in Northern Ireland Climate

Rotary Unmanned Aerial Vehicles (RUAVs) suffer in average Northern Irish winters due to heavy wind preventing vital tasks from being performed the economy, such as search and rescue or civil engineering observations. This work provides enhanced stability of RUAVs under disturbances by using metaheuristic algorithms select optimal controller gains. Previous demonstrated how Particle Swarm Optimization can be used tune controllers; this uses a machine learning algorithm (Genetic Algorithm) controller. Simulations carried out on Full State Feedback, Compensator Linear Quadratic Gaussian controllers tuned variety techniques revealed that Genetic Algorithm outperformed conventional manual tuning 20% 17% performance measured settling time. The proposed method tunes feedback gains Kalman filter Algorithm, which outperforms manually schemes “GA-Hybrid” approach. conditions required employ Reinforcement Learning an alternative for RUAV stabilization future scope is also explored.