In this paper, a novel method for designing control systems is presented. This approach defines a cost function that not only considers control error and energy but also incorporates uncertainty bounds. The control command is obtained by optimizing the cost function over a limited horizon. A key feature of this method is its simplicity in understanding and implementation. By solving complex differential equations and optimizing them within a finite horizon, transforming them into algebraic equations, an analytical and closed-form solution for the control signal is achieved in linear systems. Additionally, this method benefits from the principles of predictive control, allowing for the integrated design of control and robustness within a single formulation. This approach reduces the computational burden compared with conventional robust predictive control methods while enhancing the system’s robustness against uncertainties. It has been applied to control a time-varying, unstable static flying object with uncertainties. Simulation results indicate that, under various uncertain conditions, the proposed method demonstrates superior performance and higher robustness compared with well-known robust and classical controllers such as PID, H_∞ and GPC.