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Model-based design is well recognized in industry as a systematic approach to the development, evaluation, and implementation of feedback controllers. Model predictive control (MPC) is a particular branch of model-based design: a dynamical model of the open-loop process is explicitly used to construct an optimization problem aimed at achieving the prescribed system's performance under specified restrictions on input and output variables. The solution of the optimization problem provides the feedback control action, and can be either computed by embedding a numerical solver in the real-time control code, or pre-computed off-line and evaluated through a lookup table of linear feedback gains. This paper reviews the basic ideas of MPC design, from the traditional linear MPC setup based on quadratic programming to more a advanced explicit and hybrid MPC, and highlights available software tools for the design, evaluation, code generation, and deployment of MPC controllers in real-time hardware platforms
Alberto Bemporad (Sun,) studied this question.