As background for the course, an introductory control course and a working knowledge of matrix theory, calculus, differential equations, and computer programming is assumed.

No textbook is required.

This course will cover broadly the many aspects of constructing a model predictive controller for a real application: model formulation, constraints, integrating disturbance models, moving horizon state estimation, setpoint tracking with unreachable setpoints, and offset-free control with nonzero disturbances. The course will present in addition the theoretical analysis tools that are used to establish the closed-loop control properties and the statistical properties of moving horizon estimation: Lyapunov stability theory, the theory of random variables, and conditional probability.