%L eprints544
%T Model predictive control Design: New Trends and Tools 
%R 10.1109/CDC.2006.377490
%I IEEE
%K dynamical model; feedback controllers; linear feedback gain; lookup table; model predictive control design; open-loop system; optimization problem; quadratic programming; control system synthesis; feedback; open loop systems; predictive control; quadratic programming; table lookup
%D 2006
%B Decision and Control
%A Alberto Bemporad
%P 6678-6683
%J Decision and Control
%C 13th-15th December 2006
%X 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