Real-time HIL validation of a lyapunov-guaranteed optimal FOPID controller for quadrotor UAV trajectory tracking

This paper presents the real-time Hardware-in-the-Loop (HIL) validation of a robust 6-Degree-of-Freedom (6-DoF) Fractional-Order Proportional-Integral-Derivative (FOPID) control architecture, optimally tuned via the Artificial Gorilla Troops Optimizer (GTOA), for quadrotor Unmanned Aerial Vehicle (UAV) trajectory tracking. First, a comprehensive kinematic and dynamic model is developed, and the control architecture is designed by decoupling the UAV into fully-actuated and under-actuated subsystems through a cascaded inner-outer loop strategy. The global asymptotic stability is mathematically established via Lyapunov theory, and a dynamic Lyapunov penalty mechanism is further integrated into the GTOA objective function to enforce this stability condition across all candidate solutions throughout the optimization process. Subsequently, GTOA is applied to obtain the optimal gains that minimize the Integral of Time-Weighted Absolute Error (ITAE). Real-time HIL experiments are conducted on an industrial OPAL-RT OP4510 real-time target simulator, where the proposed GTOA-FOPID is evaluated against the African Vultures Optimization Algorithm (AVOA)-FOPID, alongside the baseline Particle Swarm Optimization (PSO)-tuned fractional-order and integer-order PID controllers. This comparative study evaluates the proposed architecture from two independent perspectives. First, the fractional-order operators are assessed against a PSO-tuned integer-order PID controller, isolating the contribution of the fractional calculus extension. Second, the GTOA is evaluated against AVOA and PSO, representing a modern metaheuristic and a standard baseline, respectively, across the identical FOPID architecture, isolating the contribution of the optimization strategy. Experimental results demonstrate the superiority of the fractional operators over the traditional integer-order PID controller. Furthermore, the proposed GTOA-FOPID achieves superior transient performance, precise trajectory tracking, and enhanced robustness under realistic hardware constraints and computational delays.