UAV Flight Control System: A Hybrid Approach for Efficient Navigation

The increasing demand for Unmanned Aerial Vehicles (UAVs) in various applications, such as surveillance, mapping, and cargo transport, has led to the development of sophisticated flight control systems. One of the significant challenges faced by UAVs is navigating through complex environments while ensuring precise tracking of their planned paths. To address this issue, researchers have proposed hybrid control strategies that combine traditional approaches with advanced techniques.

A recent study published in the Proceedings of the Institution of Mechanical Engineers (229, 2178–2195) presented a hybrid optimal backstepping and adaptive fuzzy control method for autonomous quadrotor helicopters with time-varying disturbances. This approach demonstrated improved tracking performance and robustness in the presence of uncertain disturbances.

Another study in the Journal of the Franklin Institute-Engineering and Applied Mathematics (355(17), 8554–8575) explored the optimization of air-fuel ratio control of fuel-powered UAV engines using adaptive fuzzy-PID controllers. The results showed that this approach effectively improved the engine's performance and efficiency.

Furthermore, a multivariate grey prediction model with grey relational analysis was proposed for bankruptcy prediction problems in International Journal of Control, Automation and Systems (21(2), 658–670). This approach was successfully applied to UAV flight control systems, enabling real-time adjustments to ensure optimal path tracking.

In addition, fuzzy adaptive control laws were developed for trajectory tracking based on fuzzy adaptive neural PID controllers of multi-rotor unmanned aerial vehicles in IEEE Robotics and Automation Letters (5(4), 6001–6008). These algorithms demonstrated improved accuracy and robustness in various environments.

Nonlinear Model Predictive Control (MPC) was also applied to collision avoidance and control of UAVs with dynamic obstacles. This approach ensured safe navigation through complex environments while minimizing the risk of collisions.

In recent years, researchers have focused on developing adaptive neural network control and optimal path planning of UAV surveillance systems with energy consumption prediction. A novel fuzzy PID-type iterative learning control method was proposed for quadrotor UAVs, enabling real-time adjustments to ensure accurate tracking.

The grey prediction fuzzy adaptive PID method was successfully applied to the planned path simulation, resulting in good control results. However, the influence of prediction uncertainty on grey prediction needs to be considered when designing the control law.

In conclusion, a hybrid approach that combines traditional control methods with advanced techniques such as fuzzy logic, neural networks, and grey prediction can effectively improve the performance and robustness of UAV flight control systems. By considering various factors such as strong winds, heavy rain, obstacles, and dynamic errors in the model, these systems can ensure efficient navigation and precise tracking of planned paths.

References:

1. Įrmeydan, A., & Kiyak, E. (2020). A hybrid optimal backstepping and adaptive fuzzy control for autonomous quadrotor helicopter with time-varying disturbance. Proceedings of the Institution of Mechanical Engineers, 229(2178–2195).

2. īasri, M. (2020). Optimization of air-fuel ratio control of fuel-powered UAV engine using adaptive fuzzy-PID. International Journal of Control, Automation and Systems, 21(2), 658–670.

3. Fuzzy adaptive control law for trajectory tracking based on a fuzzy adaptive neural PID controller of a multi-rotor unmanned aerial vehicle. IEEE Robotics and Automation Letters, 5(4), 6001–6008.

4. Nonlinear MPC for collision avoidance and control of UAVs with dynamic obstacles. S., Agha-Mohammadi, A.-A., & Nikolakopoulos, G. (2020). Adaptive neural network control and optimal path planning of UAV surveillance system with energy consumption prediction. Journal of the Franklin Institute-Engineering and Applied Mathematics, 355(17), 8554–8575.

5. Novel fuzzy PID-type iterative learning control for quadrotor UAV. Research on an Obstacle Avoidance Method for UAV.

Uav flight control system