This paper presents a real-time application of fractional-order PID (FOPID or PI \(^{\lambda }\) D \(^{\mu }\) ) 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 \(^{\lambda }\) D \(^{\mu }\) control, where the fractional orders \(\lambda\) and \(\mu\) 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.
