A model-free adaptive control method based on a centralized Kalman filter disturbance observer was proposed for a class of discrete-time nonlinear systems with measurement disturbances.The linearized data model of the controlled system was constructed by dynamic linearization method; according to the linearized data model and the measurement data of the sensor,an optimal centralized Kalman filter disturbance observer was designed.Finally,using the output of the observer to adjust the pseudo partial derivatives online,the control update scheme of the system was proposed.The design and analysis of the proposed scheme do not depend on any model information except input and output data,which can avoid the conventional model-free adaptive control methods being susceptible to measurement disturbances.Simulation results show that,compared with the model-free adaptive control method based on the single-sensor Kalman filter disturbance observer,the proposed model-free adaptive control method based on the multi-sensor optimal centralized Kalman filter disturbance observer has better tracking performance and larger data signal-to-noise ratio.