Performance analysis of hybrid optimization approach for UAV path planning control using FOPID-TID controller and...

Area of Science:

• Robotics and Control Systems
• Artificial Intelligence and Optimization
• Aerospace Engineering

Background:

• Unmanned Aerial Vehicles (UAVs) require efficient trajectory optimization for complex environment navigation.
• Traditional algorithms like A*, JPS, Bezier, and L-BSGF have limitations in performance and computational efficiency.
• Integrating advanced control strategies is crucial for enhancing UAV capabilities.

Purpose of the Study:

• To comparatively analyze various trajectory optimization algorithms for UAVs.
• To introduce and evaluate a novel FOPID-TID based Hybrid Archimedes Optimization Algorithm-Rider Optimization Algorithm (HAOAROA).
• To demonstrate the superiority of the proposed method in trajectory length, smoothness, stability, and computational efficiency.

Main Methods:

• Development of the FOPID-TID based HAOAROA integrating fractional-order control and hybrid optimization.
• Comparative simulation analysis against traditional algorithms (A*, JPS, Bezier, L-BSGF).
• Evaluation metrics included trajectory length, smoothness, stability, and computational time.

Main Results:

• The FOPID-TID based HAOAROA achieved a 10% reduction in trajectory length compared to traditional methods.
• The proposed algorithm demonstrated superior trajectory smoothness and overall stability.
• Enhanced dynamic response, disturbance rejection, and control precision were observed, particularly in challenging environments.
• The FOPID-TID based HAOAROA proved to be more computationally efficient.

Conclusions:

• The FOPID-TID based HAOAROA offers significant performance improvements for UAV trajectory planning in complex environments.
• Fractional-order control enhances dynamic response and precision, outperforming traditional subroutines.
• This study validates the effectiveness of hybrid optimization for advancing UAV control systems.