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Reactive power injection in smart grid distribution networks via distributed generators is envisioned to play a vital role in voltage/VAR support. In this paper, we integrate the three aspects of voltage/VAR support: modeling, state estimation and network control in a single framework. Firstly, we develop an input to state nonlinear dynamic model that incorporates power flow equations along with load and distributed generation (DG) forecasts. Then, considering an extended Kalman filter (EKF) approach for nonlinear state estimation, we analyze the impact of dropped packets on stability of estimation process. Finally, we apply separation principle locally around some known state estimates, to design a nonlinear model predictive control (NMPC) based voltage/VAR support strategy. The control problem aims to minimize the aggregate reactive power injected by DG with the following constraints: 1) voltage regulation; 2) phase imbalance correction; and 3) maximum and minimum reactive power injection by individual generators. Considering computational complexity incurred in search for the optimal solution for large scale nonlinear control problems, we propose a successive time varying linear (STVL) approximation to our voltage/VAR control problem. The control framework approach and the analytical results presented in this paper are validated by simulating a radial distribution network as an example.