Abstract :
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
Keywords :
control system synthesis; feedback; open loop systems; predictive control; quadratic programming; table lookup; dynamical model; feedback controllers; linear feedback gain; lookup table; model predictive control design; open-loop system; optimization problem; quadratic programming; Adaptive control; Design optimization; Electrical equipment industry; Embedded computing; Feedback control; Industrial control; Linear feedback control systems; Predictive control; Predictive models; System performance;
