Enhanced trajectory tracking and robustness in magnetic levitation via takagi-sugeno fuzzy control: experimental approach

Abstract

Abstract Robust control of magnetic levitation (maglev) plant remains a significant challenge due to its inherent non-linearities and uncertainty to exogenous perturbations, though maglev technology has a wide range of usages, from high-speed trains to advanced robotics. To solve these problems and improve the maglev system’s trajectory-tracking performance and robustness, this research proposes a control technique that involves synthesizing a T-S fuzzy controller using the parallel distributed compensation (PDC) method. The controller design is further augmented with a velocity-compensation technique to enable smooth and frictionless ball levitation in a maglev system. The gravitational bias acting on the maglev system is controlled by integrating the feed-forward controller ( $$F_f$$ ) with the PDC-TS fuzzy scheme. The Lyapunov function candidate and linear matrix inequalities (LMIs) are explored to determine the proposed TS fuzzy scheme’s global asymptotic stability. Finally, the effectiveness of the control technique is experimentally evaluated for several test cases using hardware-in-loop (HIL) testing on the maglev system. The results corroborate that the T-S fuzzy control strategy offers robustness and trajectory tracking of the system with stable levitation over the traditional PIV scheme.