1.Model predictive control: regulation problem, dynamic programming, lin- ear quadratic regulator, constraints, infinite horizon, LQR, constrained regulation. 
2.State estimation: least-squares estimator, Kalman filter, observability and convergence. 
3.Putting regulation and estimation together, industrial practice, distur- bance models, and offset. 
4.Nonlinear MPC. introduction, stability, Lyapunov function theory, distur- bances and robust stability, nominal stability, suboptimal MPC, inherent robustness of optimal and suboptimal MPC, some examples. 
5.Nonlinear moving horizon state estimation. full state estimation, moving horizon estimation with zero prior weighting, nonzero prior weighting, constrained estimation. 
6.Economic MPC. problem formulation and properties, periodic constraints. open research issues. 
7.Other topics: Suboptimal MPC, MPC with discrete actuators.