Model-Based Control: Literature Review

Abstract

Model-based control is a generic term for a widely used class of process model-predictive control (MPC) algorithms. Model-predictive control has emerged as a powerful practical control technique during the last decade. Its strength lies in its use of step response data, which are physically intuitive, and that it can handle hard constraints explicitly through on-line optimization. Various MPC techniques such as dynamic matrix control (DMC) (Cutler and Ramaker, 1980), model algorithmic control (MAC) (Rouhani and Mehra, 1982), and internal model control (IMC) (Garcia and Morari, 1982) have demonstrated their effectiveness in industrial applications. As described in chapter one, a process model and a reference trajectory are two of the most essential characteristics of model-based control algorithms such as GMC. Recently, an interesting application based on neural model-predictive control (NMPC) method was proposed by Ishida and Zhan (1995) for the one-step predictive control of MIMO processes.