Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
LEARNING OUTCOMES
After the course, the participant will understand:
- Unconstrained and constrained numerical optimsation (Newton's type methods, automatic differentiation);
- Discrete-time optimal control (Dynamic programming: Basic, iterative and differential variants);
- Continuous-time optimal control (Hamilton-Jacobi-Bellman, Pontryagin approaches, direct approaches);
- On-line optimal control (Model predictive control, moving-horizion estimation and control).
Credits: 5
Schedule: 12.01.2021 - 19.02.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Francesco Corona
Teacher in charge (applies in this implementation): Francesco Corona
Contact information for the course (valid 19.12.2020-21.12.2112):
Lecturer, slack workspace, and office hours
Francesco Corona (firstname.lastname@aalto.fi ) | E331 Kemistintie 1 (¬L¬)
Office hours | WED 12:00-13:00 and FRI 15:00-16:00 | Use this Zoom link
Communication via slack workspace | Click-me to join (Lecture links, etc)
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
Valid 01.08.2020-31.07.2022:
We study the mathematical principles of optimal control to manipulate the dynamic behaviour of process systems and the numerics used for its solution. The course aims at bringing understanding on how to combine numerical optimisation with dynamical systems theory to formulate and solve optimal control problems in both discrete- and continuous-time. We develop the topic in general application domains in chemical and bio-chemical engineering.
Applies in this implementation:
CHEM-E7225 is an introductory course on optimal control: We study the mathematical principles of optimal control to manipulate the dynamic behaviour of process systems and the numerics used for its solution. The course aims at bringing understanding on how to combine numerical optimisation with dynamical system theory and numerical simulation to formulate and solve optimal control problems in both discrete- and continuous-time. We develop the topic in general application domains in chemical and bio-chemical engineering.
- Introduction/refresher on dynamic process models, numerical simulations, and optimisation (classes of models, numerical integration schemes, and optimisation problem classes);
- Root-finding with Newton-type methods (Definitions, convergence rates and contraction);
- Nonlinear optimisation (Definitions, first- and second-order optimality conditions) and Newton-type algorithms (Equality and inequality constrained problems);
- Discrete-time optimal control (Formulation and analysis);
- Dynamic programming (Discrete-time discrete-space problems, linear-quadratic problems, infinite-horizon problems, the linear quadratic regulator, the gradient of the value-function);
- Continuous-time optimal control (Formulation, analysis, and numerical approaches);
- The Hamilton-Jacobi-Bellman equation (Dynamic programming in continuous-time, linear-quadratic control and Riccati equations) and the Pontryagin maximum principle.
Assessment Methods and Criteria
Valid 01.08.2020-31.07.2022:
Home assignments and/or project work, and/or exam.
Applies in this implementation:
Course evaluation: To pass CHEM-E7225 Home Edition, you must return the solution to all the exercises (80%) and participate (20%) to the course activities.
- You get to pick your deadline for returning the exercises. Your deadline must be before JUN 04 (2021) at 23:59:59.
- Once chosen, you must communicate the deadline to the lecturer via email, and stick to it.
- You must communicate the chosen deadline by FEB 19 (2021) at 23:59:59 (Do not forget to do it, if you want to pass the course).
- Delayed submissions will be penalised: Your final score will drop by one (1) Oodi point every 24 hours after deadline.
Grading scheme (0-100 MC to 0-5 Oodi conversion)
- 5 <-- [88, 100]
- 4 <-- [76, 88)
- 3 <-- [64, 76)
- 2 <-- [52,64)
- 1 <-- [40,52)
- 0 <-- [00,40)
About the exercises (80%)
- TBA
Upload a single file, only use the PDF format. (If you have work multiple files, merge them. If you use MSWord or else, save as PDF. If you use MSWord or else and you have multiple files, ...).
About participation (20%)
- TBA
Collaboration policy: We encourage you to collaborate in figuring out answers and help others solve the problems, yet we ask you to submit your work individually and explicitly acknowledge those with whom you collaborated. We are assuming that you take the responsibility to make sure you personally understand the solution to work arising from collaboration.
Workload
Valid 01.08.2020-31.07.2022:
Lectures (32h) and Exercises (16h)
Home assignments and independent study (80h)
Exam (4h)
DETAILS
Study Material
Valid 01.08.2020-31.07.2022:
- Nocedal, J. and Wright, S. J., Nonlinear optimization, 2006;
- Bertsekas, D. P., Dynamic programing and optimal control, vol. I & II, 2017 & 2012;
- Bertsekas, D. P., Reinforcement learning and optimal control, 2019;
- Betts, J. T., Practical methods for optimal control and estimation using nonlinear programming, 2009;
- Rawlings, J. B., Mayne D. Q., Diehl, M., Model predictive control, 2017.
Substitutes for Courses
Valid 01.08.2020-31.07.2022:
CHEM-E7165 Advanced process control methods
Prerequisites
Valid 01.08.2020-31.07.2022:
Conservation laws, ordinary differential equations, linear algebra and elements of programming and numerical methods.
SDG: Sustainable Development Goals
6 Clean Water and Sanitation
7 Affordable and Clean Energy
8 Decent Work and Economic Growth
9 Industry, Innovation and Infrastructure
11 Sustainable Cities and Communities
12 Responsible Production and Consumption
13 Climate Action
- Teacher: Corona Francesco