Fractional-order PID control for elevation and azimuth in a twin rotor system

Area of Science:

• Control Engineering
• Robotics
• Applied Mathematics

Background:

• Conventional PID controllers face limitations in optimizing complex systems like twin rotors.
• Fractional-order control (FOC) offers enhanced flexibility for tuning system dynamics.

Purpose of the Study:

• To implement and validate a fractional-order PID (FOPID) controller for a twin rotor system in real-time.
• To optimize FOPID controller parameters using metaheuristic algorithms for improved performance.

Main Methods:

• Linear model identification using a black-box approach.
• Implementation of FOPID control with fractional orders λ and μ for integral and derivative terms.
• Optimization of controller parameters using Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Nelder-Mead (NM) methods.
• Minimization of time-domain performance metrics (IAE, ITSE, ISE, ITAE).

Main Results:

• The GA-optimized FOPID controller achieved an IAE of 180.33 for elevation and 109.2 for azimuth.
• GA-based FOPID significantly outperformed GA-based IOPID (IAE of 247.05 for azimuth).
• PSO and NM-based FOPID tuning showed the least performance index across all metrics compared to GA.

Conclusions:

• FOPID control significantly enhances control precision and stability in twin rotor systems.
• Fractional-order control (FOC) demonstrates strong potential for real-time applications.
• Metaheuristic optimization algorithms are effective for tuning FOPID controllers.