<p>This paper presents an experimentally validated hybrid optimization-based fuzzy logic controller (H-FLC) for trajectory tracking control of a magnetic levitation (maglev) system. A hybrid coyote optimization-based big-bang big-crunch algorithm is proposed to optimally tune the FLC scaling factors, addressing the nonlinear dynamics and sensitivity limitations inherent in maglev systems. The hybrid formulation enhances exploration–exploitation balance and accelerates convergence compared to conventional big-bang big-crunch-based tuning. The optimized controller is experimentally evaluated on a laboratory-scale maglev setup under multiple reference trajectories, including step, square, sinusoidal, and sawtooth signals. Performance is assessed using standard error-based indices. Experimental results demonstrate stable and accurate trajectory tracking, with the proposed H-FLC achieving up to 99% reduction in integral square error and 83% reduction in root mean square error compared to baseline controllers. Comparative analysis with existing control strategies further confirms the superior tracking performance and robustness of the proposed approach, highlighting its potential for precision maglev applications.</p> Graphical abstract <p></p>