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

This paper presents a real-time application of fractional-order PID (FOPID or PI[Formula: see text]D[Formula: see text]) control for a twin rotor system, optimizing performance beyond conventional PID approaches. A linear model identification is first performed using a black-box approach, with a detailed examination of the system's static properties. The primary aim is to implement PI[Formula: see text]D[Formula: see text] control, where the fractional orders λ and μ correspond to the integral and derivative components, respectively, offering enhanced flexibility in system dynamics tuning. The proposed control strategy is validated through experiments on a laboratory-scale twin rotor benchmark. Controller parameters are optimized using advanced algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and the Nelder-Mead (NM) method. These algorithms minimize time-domain performance metrics such as Integral of Absolute Error (IAE), Integral of Time-weighted Squared Error (ITSE), Integral of Squared Error (ISE), and Integral of Time-weighted Absolute Error (ITAE). Notably, the optimized GA-based FOPID controller achieves an IAE performance index of 180.33 for the FOPID in elevation. The GA-based FOPID tuning is particularly effective for IAE performance in the azimuth, yielding a value of 109.2, compared to the GA-based IOPID, which results in a value of 247.05. Additionally, the least performance index is observed when comparing the PSO and NM-based FOPID tuning across all performance indexes. These results demonstrate that the FOPID controller significantly enhances control precision and stability in the twin rotor system, highlighting the potential of fractional-order control (FOC) in real-time applications.