A Model Predictive Control Framework Based on Additive State Decomposition for Nonlinear Systems with Input Saturation

This paper proposes a control framework based on Additive State Decomposition (ASD) and Model Predictive Control (MPC) for nonlinear systems with input saturation. ASD is used to divide the system into a linear primary system and a nonlinear secondary system. The primary system is controlled by MPC to ensure accurate trajectory tracking under constraints, while the secondary system compensates for nonlinearities and saturation using robust strategies. This method improves tracking accuracy compared to linearization-based MPC. Simulation results validate the efficacy of the ASD-based MPC framework in handling nonlinearities and input saturation, offering a robust solution for constrained nonlinear control problems. A discussion of future research directions regarding the method is provided before the concluding section of the paper.