<p>This paper puts forward improved inclined plane optimization (IIPO) algorithm to design fuzzy logic controller (FLC) for reference tracking in a magnetic levitation (maglev) system. Maglev system plays a major role in contactless tracking and propelling objects. This leverages on the principle of electromagnetism and is highly nonlinear in nature. Hence, implementing FLC for maglev system will play a significant part in excluding sub-optimal results generated due to the bode sensitivity integral problem. The highly nonlinear nature of maglev system is proposed to be controlled by IIPO-based FLC design. The motivations for IIPO-based FLC over simple IPO-based FLC are (1) improved convergence performance, (2) reduced control complexity in achieving optimal results, and (3) simplified algorithm mechanism. Hence, IIPO algorithm is used to tune normalization and denormalization factors of FLC for precise reference tracking. The performance of the proposed control scheme is validated experimentally for step, square, sine, and sawtooth reference signal. The results show that the proposed control scheme reduces the integral square error (ISE) values by approximately 90% compared to simple IPO-based FLC, demonstrating precise and stable reference tracking. This translates into reduced oscillations and faster stabilization. The key metrics of the analysis include ISE, integral time square error, integral absolute error, integral time absolute error, and root mean square error, which confirms the superiority of the proposed IIPO-based FLC design. The drastic improvements in all the error metrics showcase the robustness, efficiency, and adaptability to varying reference signals. Its ability to maintain high accuracy and low error metrics highlights the advantage of the proposed IIPO-based FLC design in a wide range of operational scenarios, in comparison with the state-of-the-art techniques.</p>