TTK39 Theory and Practice of Model Predictive Control

3.75 study points

Teacher: Associate Professor Dinesh Krishnamoorthy

Model Predictive Control (MPC) is one of the most successful and widely adopted techniques in modern control theory. From process industries and energy systems to robotics, automotive applications, and biomedical engineering, MPC has become a key enabler of high-performance, constraint-aware decision making in complex dynamic systems.At its core, MPC is a flexible control paradigm that determines control actions by solving an optimization problem in real time. This allows engineers to directly encode performance objectives in the time domain, systematically handle multivariable interactions, and explicitly enforce constraints on inputs, states, and outputs—capabilities that are essential in many real-world systems.

In this course, you will learn both the fundamental principles and the practical implementation of MPC.

• Design of model predictive control (linear, nonlinear and economic MPC)
• Theory: Nominal Stability and recursive feasibility
• Application: Programming exercise

Chapters 2 and 8 from Rawlings, J., Mayne, D.Q., & Diehl, M. Model Predictive control: Theory Computation and Design, 2nd Edition.