This is a course on model predictive control (MPC), or optimal control of systems with hard constraints on states and control inputs. Contents; Properties of discrete-time linear systems in state-space form; optimal state transfer by linear and quadratic programming; design of linear-quadratic optimal controllers using dynamic programming; model predictive control and the receding horizon principle; dealing with state and control constraints; design and tuning of model predictive controllers and receding-horizon estimators; output feedback MPC; reference-following MPC; stability analysis of MPC controllers; implementation as explicit nonlinear feedback law or by real-time optimization.
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