<p>Existing model predictive control schemes that employ the nominal system as the prediction model can guarantee closed-loop robustness. However, since disturbance information is neglected, the prediction model cannot accurately reflect the actual system dynamics, which consequently degrades control performance. In this work, a tube-based model predictive control approach, incorporating estimated disturbance information, is designed for discrete-time nonlinear systems. The disturbance estimation is obtained through a disturbance observer and incorporated into the prediction model, enabling a more accurate system state prediction. A robust control invariant set is then constructed by solving linear matrix inequalities, which bounds the error between the actual system state and the predicted state. A novel framework for tube-based MPC with the estimated disturbance information is introduced. Furthermore, both recursive feasibility and closed-loop stability are rigorously ensured under the proposed MPC method. The proposed approach is validated through the simulation studies performed on a Continuous Stirred Tank Reactor (CSTR) system.</p>