Abstract <p>This paper uses an improvised version of the teaching–learning-based optimization (TLBO) algorithm for tuning a fuzzy PID controller aimed at improving the dynamic control of a nonlinear helicopter system under uncertain and time-varying conditions. The study contributes to the broader field of control theory and automation by addressing the challenges of autonomous flight control through intelligent optimization. External disturbances are introduced using high-speed fans that create turbulent airflow thus replicating real-world aerodynamic conditions within a laboratory environment. The helicopter model is developed using the Lagrangian approach representing a complex nonlinear multivariable system. The fuzzy PID controller tuned via the proposed TLBO method effectively reduces yaw and pitch tracking errors and improves disturbance rejection. The optimization framework adapts dynamically to environmental variability narrowing the gap between simulated and practical control performance. Comprehensive MATLAB/Simulink simulations confirm that the proposed control strategy enhances trajectory tracking robustness and computational efficiency compared to conventional approaches thereby demonstrating its suitability for advanced automation and real-time control applications.</p>