This paper presents two adaptive controllers for vibration reduction in aircraft landing gear: a fuzzy adaptive controller (FAC) and a classic model reference adaptive controller (MRAC). The control input is the active force generated by the landing gear actuators. Both controllers are designed for high-order vibration models to decrease vibration amplitude and improve passenger comfort. A 6-DOF dynamical state-space model of the landing gear is considered. This model includes lumped uncertainties such as external disturbance, measurement errors, unmodeled dynamics, and parametric uncertainties. In both controllers, the parameters are adjusted to guarantee Lyapunov stability of the overall system. The fuzzy adaptive controller uses a Mamdani-type fuzzy inference system with a singleton fuzzifier and center of gravity defuzzification as a universal approximator. Compared to existing methods, the main advantages of the proposed approach are: 1) closed-loop stability is proven via Lyapunov analysis, 2) fuzzy parameters are adapted online without offline training, 3) tracking error and vibration converge to zero, and 4) robustness against lumped uncertainties is achieved. The results show approximately 20% improvement with the proposed FAC over the classic MRAC. The proposed method effectively absorbs vibrations and promotes passenger comfort under external disturbances.