<p>A state observer-based model predictive control (SOBMPC) strategy based on the state-space model is proposed in this paper. This control approach aims to achieve both accurate system state estimation and robust model predictive control performance simultaneously for dynamic control systems. Firstly, a mathematical model in state-space form is developed, and this state-space representation is transformed into a supplementary system model. Then, a discrete-time supplemental state observer (SSOB) is developed to estimate the unavailable system states. A robust model predictive control (MPC) strategy is formulated based on the Lyapunov stability theorem. Finally, the SOBMPC has been successfully integrated with the SSOB and MPC in this paper. An electrical drive system is presented as an application example to validate the control characteristics of the SOBMPC. The simulation results demonstrate that the proposed SOBMPC exhibits strong capabilities in system state estimation and robust performance in model predictive tracking control simultaneously. The primary contribution of this paper is the proposal that SOBMPC exhibits robust characteristics of MPC and offers precise system state estimation. The effectiveness of the proposed control strategy is validated through a case study involving an electrical drive system.</p>