Robust Model Predictive ControlDavide ManniniNominal model predictive control (MPC) is a feedback control scheme that predicts the future states of a dynamic system, using a process model, over a finite time window. Based on the predicted and current states, the control scheme optimizes an objective function to determine an optimal manipulated variable profile. The term nominal MPC refers to the assumption that the system is deterministic, meaning that the predicted behavior from a nominal model is identical to the actual behavior. If this is not the case and unknown disturbances cause a system to be uncertain, then robust model predictive control (RMPC) is a suitable control scheme. While RMPC introduces additive disturbances in the model to account for an uncertain system, its practical applications have so far been limited. Implementation of RMPC for complex systems is generally limited by the optimization problem, which is time consuming and computationally expensive. The goal of this project is to reduce the computational burden associated with RMPC for online implementation. The primary objective is to develop a novel efficient method to solve the RMPC optimization problem. |