This paper addresses the robust model predictive control (MPC) of constrained linear discrete-time systems in the presence of bounded disturbances. The key innovation is the inclusion of the initial state of the model as a decision variable in the online optimal control problem, which uniquely distinguishes it from conventional MPC. This approach ensures that the value function is zero in a disturbance invariant set \( Z \), leading to robust exponential stability of \( Z \) for the controlled system. The resulting online algorithm is a quadratic program with similar complexity to conventional MPC. The paper also provides a detailed analysis of the properties of the value function and the robustness of the proposed controller, supported by a numerical example. The main result demonstrates that the set \( Z \) is robustly exponentially stable for the controlled uncertain system, with a region of attraction \( X_N \). The controller is simple and effective, but its effectiveness is limited to linear systems due to the linear nature of the problem.This paper addresses the robust model predictive control (MPC) of constrained linear discrete-time systems in the presence of bounded disturbances. The key innovation is the inclusion of the initial state of the model as a decision variable in the online optimal control problem, which uniquely distinguishes it from conventional MPC. This approach ensures that the value function is zero in a disturbance invariant set \( Z \), leading to robust exponential stability of \( Z \) for the controlled system. The resulting online algorithm is a quadratic program with similar complexity to conventional MPC. The paper also provides a detailed analysis of the properties of the value function and the robustness of the proposed controller, supported by a numerical example. The main result demonstrates that the set \( Z \) is robustly exponentially stable for the controlled uncertain system, with a region of attraction \( X_N \). The controller is simple and effective, but its effectiveness is limited to linear systems due to the linear nature of the problem.