Model predictive controller (MPC), even when well projected, is minded to unmeasured disturbances along operation. It is usual to observe offset between controlled variables and their set points in result of those disturbances. The undesirable offset may cause loss of quality and profits, unconformity of the final product or, at the furthest cases, the instability of the process. Some alternative methods to prevent offset were shown in literature. One of them is the application of state estimators to predict model mismatch in nonlinear processes. Huang et al. (2009) demonstrate the use of recursive observers to correct and stabilize MPC controllers. In the present work, an adaptive linear model is proposed to estimate the error between the predictive model and process response to help MPC to compute future control actions. Every sample time, this adaptive model is adjusted based on a Kalman Filter (a state observer), using past measured values of the process variables. The error found is then added to model predictions to find corrected actions for the control horizon of MPC. The proposed MPC was applied to an experimental system of interacting tanks, controlling their levels by manipulating feed flow rates. The obtained results were compared to the original MPC controller (no corrections) in situations of servo and regulatory control, under occurrence of unmeasured disturbances and/or set point changes. As result, it was observed that controller response was improved in the presence of the adaptive model proposed, reducing significantly the offset in all tests carried out. It was found a reduction of more than 40 % of the Integral of Squared Errors (ISE) performance index, due to eliminating the lack of adjustment between plant and its mathematical modelling Ver menos