MECHTRON 4AX3, Term I 2026-2027
Predictive and Intelligent Control
Instructor
Dr. M. v. Mohrenschildt, ITB 164, mohrens
Office hours: To be determined
My main way to communicate is email. Do not sent me messages in other systems, I am not monitoring them.
Lectures, Tutorials
Lectures are in person
MECHTRON 4AX3 Tu, Th, Fr 11.30-12.20
Tutorial:
- Tu 1:30PM - 2:20PM
- Tu 1:30PM - 2:20PM
Note:
- There will be no no Tutorials in the first week.
- Midterm: Oct 27, 11:30-12:20 (room to be determined)
Description
The course will present key aspects of advanced control and Machine Learning. We will examine the
principles common to both and the difference in formulation of the two approaches.
Major Topics
The following are the major topics (12 major topic, about 3 lectures per topic
flexible depending on audience).
There will be 35 lectures + 1 midterm lecture and 12 tutorials.
- Review of mathematical concepts (Control, Linear Algebra, Probability)
- State space control
- Observers
- Bayesian Reasoning
- Estimation, Regression
- Statistical concepts, optimal prediction
- Kalman Filtering
- Markov Chains and Bellman Equation, Dynamic programming (AI and control)
- Re-enforcement learning, Bellman
- Optimal control, LQR, Ricatti
- Model Predictive Control
- Machine learning approaches
This course introduces several concepts used in systems engineering, predictive control and artificial intelligence. A variety of techniques including prediction and estimation, linear models, basic optimization techniques, Monte Carlo techniques, neural networks, and clustering are introduced. The techniques are applied to predictive and smart systems by the example of model predictive control and intelligent control, classification and decision-making. The course is intended for engineering students with understanding in signals and systems and control.
Prerequisite(s): MECHTRON 3DX4 or SFWRENG 3DX4 or IBEHS 4A03
Teaching Assistants
- Winnie Trandinh trandint@mcmaster.ca
- Ryan Gowland gowlandr@mcmaster.ca
Course Information on Web
- The study material and the latest information about the course is here:
HERE
- This document is located at:
http://www.cas.mcmaster.ca/~mohrens/4ax3/outline.html .
Grades, Assignments, and Exams
Assignments
There will be 5 assignments
Graduate Attributes
The major topics are listed above
Learning Objectives
Learning objectives are measured and reported to the CEAB as part of the accreditation process. The numbers in brackets define the mapping from indicators to competencies.
- Students should know and understand
- State Space Representations (1.1)
- Different approaches to linear least square and regression (1.1, 3.1)
- Observers, estimation (2.1)
- Concept of Kalman filtering (2.1, 1.4)
- Bellman equation (2.1, 5.1)
- Optimal Control principles (1.4)
- Reinforcement learning (1.4)
- Students should be able to
- Perform state space control simulations in matlab and C++ (5.1)
- Place poles to shape a system response (1.4)
- Apply Kalman filtering to simple problems (2.1, 5.1)
- Develop a finite horizon optimal controller (5.1)
- Implement machine learning principles in C++ (5.1)
Approved Advisory Statements
The following statements are required to be included in all course outlines by the Undergraduate Course Management Policy.
Academic Integrity
You are expected to exhibit honesty and use ethical behavior in all aspects of the learning process. Academic credentials you earn are rooted in principles of honesty and academic integrity.
It is your responsibility to understand what constitutes academic dishonesty.
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the grade of zero on an assignment, loss of credit with a notation on the transcript (notation
reads: “Grade of F assigned for academic dishonesty”), and/or suspension or expulsion from
the university. For information on the various types of academic dishonesty please refer to the
Academic Integrity Policy, located at
https://secretariat.mcmaster.ca/university-policies-procedures-guidelines/
The following illustrates only three forms of academic dishonesty:
- Plagiarism, e.g., the submission of work that is not one’s own or for which other credit
has been obtained.
- Improper collaboration in group work, including not doing your share of the work.
- Copying or using unauthorized aids in tests and examinations.
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Additional Statements
Generative AI
Generative Artificial Intelligence (AI) is permitted in this course for all deliverables. However,
the use of AI has to be fully transparent, and all individuals are accountable for the work
they submit. Transparency means that the use of AI is clearly indicated and documented.
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concerns regarding inclusion in our Faculty, in particular if you or one of your peers is experiencing harassment or discrimination, you are encouraged to contact the Department Chair,
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