625.621.8VL - Modern Control Systems
Description
Modern control is based on the concept of a state for modeling dynamical systems. This course considers both continuous-time and discrete-time systems, focusing primarily on the case of a digital controller. The course starts with the study of deterministic systems, covering observability, controllability, stability, and state-variable feedback. Consideration of stochastic disturbances leads to Kalman filter algorithms for state estimation and system identification. Students will develop computer simulations involving classical Kalman filters, numerically superior square-root algorithms, and extended and unscented Kalman filters for systems that are nonlinear or have non-Gaussian or non-additive disturbances. The concept of state is then extended to control based on reinforcement learning. The course also provides an introduction to optimal control. Prerequisites: Undergraduate courses in linear algebra and either multivariate calculus or signals and systems.
Instructor
Fred Fontaine
Course Structure
The course materials are divided into modules which can be accessed by clicking Course Modules
on the course menu. A module will have several sections including lecture notes and assignments.
You are encouraged to preview all sections of the module before starting. There are four modules,
with one or two assignments attached to each module. I will give you at least one week's notice
before an assignment is due. The precise due dates may shift depending on the pace we set.
During each class session, we will complete about 1 ½ parts. You should regularly check the
Calendar and Announcements for assignment due dates.
Course Topics
Module I: Signals and Systems.
1. Introductory Concepts.
2. Lp-norms and Stability.
3. Laplace and z-Transforms.
4. Time Domain Analysis: Continuous-Time.
5. Time Domain Analysis: Discrete-Time.
6. Frequency Domain.
7. Sampling and Digital Control of Analog Systems.
Module II Deterministic Dynamical Systems
1. Topics from Linear Algebra.
2. State-Space Models.
3. Controllability.
4. Observability.
5. State Variable Feedback.
Module III Stochastic Dynamical Systems
1. Statistical Concepts and Estimation Theory.
2. Stochastic Dynamical Systems.
3. Basic Kalman Filter Algorithm.
4. Square-Root Kalman Filter.
5. Tracking Applications.
6. Recursive Least Squares.
7. Unscented Kalman Filters.
8. Simulation Methods.
Module IV Reinforcement Learning for Control Applications
Course Goals
Textbooks
Students will be provided a detailed set of lecture slides, and will have access to recorded videos. The material covered in the text cannot be found in a single textbook. Several additional references, including online resources and articles from peer reviewed technical journals, will be identified as the course progresses.
Required Software
Students are expected to have some background in using a scientific computing software package, preferably MATLAB. Python is an alternative, but using MATLAB is probably more convenient for most of the assignments. If you intend to use a different software package, discuss it with me for approval first.
Student Coursework Requirements
Although recordings of the lectures will be made available for review, it is best if you attend the lectures live.
There will be a total of 6 assignments: one for Module I, two for Modules II and III each, and one for Module IV. I will identify the points for each assignment, which will total 100 points.
Most of the assignments will focus on exploration of the subject with computer simulation, though there may be some guided questions that develop theory.
homework. If you do not understand some of my comments, please CONTACT ME.
For reference, suppose an assignment is worth 10 points. Then the grading generally follows these guidelines:
10= A: All questions are addressed. Answers are precise, neither too verbose nor too terse.
Comments and explanations are clear, insightful and to the point. Good coding techniques are used, the code works, graphs
are clear and labeled well, and good comments and analysis of results are provided.
9= A-: The work reflects a solid understanding of the material, but there are some minor errors, or
room for improvement e.g., in coding style or more precise explanations.
8= B: There are some errors but overall good effort and results are mostly correct. The code works,
and good coding style is used but there is room for improvement. The work reflects understanding
of the material.
7= C: The work shows that some concepts are not understood. Some explanations are wrong or
missing. Either the code does not work, or poor coding style is used. All parts are at least
attempted.
6= C-: Some work is not completed. The work reflects partial understanding. There are some
substantial errors.
≤5= F: Work reflects minimal effort to complete the assignment or understand the material.
Significant portions of the work are missing.
YOU MUST SUBMIT ALL CODE. This could be an executable file (.m in MATLAB, or Jupyter Notebook for Python, etc.), or a printout of the code.
Just results and graphs are NOT sufficient, and in fact you will receive ZERO credit if the code is not included.
If you submit .m or .ipynb files, they should be executable on my computer, so be careful the code actually works! Be sure to include all figures
I need to see, or your code should automatically generate the necessary results when I run it.
The due time for each assignment is strict. Exceptions would be granted only under extenuating
circumstances.
THE WORK IS TO BE YOUR OWN. You can discuss course topics and the assignments in a general sense with other students, but you really should be working independently. Communicate with me! I tend to give many hints and tips. Learning is an iterative process and you will get the most out of this course if we engage in a continual discussion as you work through the assignments.
Grading Policy
Grading Scale
90- 100 =A
80-89 = B
70-79 = C
60- 69 = D
< 59 = F
Academic Policies
Deadlines for Adding, Dropping and Withdrawing from Courses
Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.
Academic Misconduct Policy
All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.
This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.
Students with Disabilities - Accommodations and Accessibility
Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.
For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.
Student Conduct Code
The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically.
For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/
Classroom Climate
JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity.
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).
Course Auditing
When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.