[E7225] Advanced Process Control D
AALTO UNIVERSITY ⬥ WINTER 2026
This course introduces optimization-based methods to automatically operate process systems. The main goal is to learn how to combine numerical optimization and dynamical systems theory to design digital solutions (i.e., feedback controllers) to control dynamical systems to optimally achieve high-level objectives. The focus is on model-based and receding-horizon control methods, with application domains in chemical and biochemical engineering.
Learning Outcomes
Upon completing the course, the student should be able to
- Simulate the transient behavior of process systems, using both nonlinear and linear(ized) models
- Understand key concepts and solution methods in nonlinear programming
- Formulate and solve optimal control problems (incl. constrained systems)
- Implement/code optimization-based control algorithms
The course is graded based on 5 weekly homeworks (40%) and 1 final assignment (60%). There is no final exam.
Program
| L01 | Introduction | [Notes] | |
| L02 | Dynamic Systems - Models and simulation | [Notes] | [Homework] |
| L03 | Nonlinear Programming (A) | [Notes] | |
| L04 | Nonlinear Programming (B) | [Notes] | [Homework] |
| L05 | Model Predictive Control - Introduction (A) | [Notes] | |
| L06 | Model Predictive Control - Introduction (B) | [Notes] | [Homework] |
| L07 | Model Predictive Control - Constraints | [Notes] | [Homework] |
| L08 | Model Predictive Control - Noise/Disturbances | [Notes] | |
| L09 | Model Predictive Control - State estimation | [Notes] | [Homework] |
| L10 | Model Predictive Control - Output feedback | [Notes] | |
| L11 | (Seminar) - Model Predictive Control of Water Resource Recovery Facilities | [Slides] | |
| A01 | Final Assignment | [Assignment] |
The homeworks are accompanied by warm-up exercises implemented in Python (required packages: [requirements.txt]).
Further Reading and Cool Links
The material is mostly based on the following textbooks:
- Model Predictive Control - Theory, Computation, and Design (2024) by Rawlings, Mayne, and Diehl
- Predictive Control for Linear and Hybrid Systems (2017) by Borelli, Bemporad, and Morari
- Convex Optimization (2004) by Boyd and Vandenberghe