The Ohio State University
Dept. Electrical and Computer Engineering

ECE 7858 Intelligent Control

(Current LONG Subtitle: Systems and Control Theory and Engineering for Social, Economic, and Political Systems)

Instructor: Prof. Kevin Passino 416 Dreese Laboratory,

Office Hours: Set an appointment via email and/or talk to me before or after class.

Scheduling: This course is offered in Spring semester of even numbered years.

Textbook (required):

You have a choice! You pick one of the following two textbooks, purchase it, read cover-to-cover this semester, and write a “book report” on it near the end of class (integrating ideas from class and the book). Depending on your interest in the subject matter of this course, with the approval of the instructor, you may propose an alternative textbook/book report; however, the subject matter of the proposed book must be the “social sciences” (psychology, social work, sociology, anthropology, economics, and politics) or one that deals with human groups (e.g., public health).   The two books are (links to, one place you may want to purchase them from):

Dale and Smith, Human Behavior and the Social Environment: Social Systems Theory, Allyn and Bacon, 7th Edition, 2013. This book contains a systems-theoretic view of "social work," the profession that focuses on helping individual people and groups of people.

Forsyth, Group Dynamics, 6th Edition, Cengage Learning, 2013. This book covers human groups, using ideas from many fields, a large range of group sizes, and group objectives.  The author’s background is “social psychology.”

Of course, the enthusiastic student may want to buy both, and read both for this class.  You will notice that there are no mathematical equations in either book, only some diagrams, and words. But, these form some important foundations for human social dynamic systems (of course there are other versions of systems theory for human groups, but these are two key books in the area).  I recommend that after finals in Autumn Semester 2013 you buy the book and read it over the Christmas break, before the class starts. That will give you a great head start on understanding many of the ideas covered in the class, in an easy-to-read format.

Relevant Texts (not required):

Pieces of all four of these "relevant texts" will be used in this course to argument the material out of the course textbook(s).

Software for Simulation:

We will be using one or two of the following four packages, depending on your choice:

  1. Matlab Simulink, Matlab Stateflow (OSU has site license so you have free access to both those packages), and programming in Matlab via .m files.
  2. Netlogo for agent-based simulation (free download), or the likes.

In some cases I may require the use of one of these packages, but in other cases I will allow you to choose which package you want to develop a simulation in. Some of you already know Simulink and .m files, but the others are probably new to you. Right now, you could download Netlogo and test out some of the "sample models" (run the simulations; it can output a CSV file that Matlab can read for plotting; you should make that work now). Next, make sure you have access to the Matlab products and start reading about them or doing the "demos" or "examples.”

History: The field of intelligent control has evolved significantly over the years as progress on theory, techniques, and applications has been made. Generally, the field largely started out at the individual intelligence level (typically thought to model some aspect of the intelligence of a single human) with fuzzy control, neural networks, planning systems, attentional systems, and with a heavy focus on learning methods for all of those. Evolutionary methods (i.e., the genetic algorithm) have been used for design of all these individual intelligent systems (and groups of such systems). Significant work has been done on stability analysis of such intelligent controllers when used in closed-loop feedback control (especially for adaptive fuzzy/neural control). While all that work was occurring there was an undercurrent of work on "hierarchical intelligent autonomous controllers" (very general compositions of the above intelligent systems, including distributed ones). But, as the understanding of the "biomimicry" of individual intelligence-focused methods matured, there was a shift to distributed intelligent systems and control, especially ones that were more tractable than the original hierarchical intelligent control methods, with corresponding applications (e.g., autonomous robot groups). Driven by the spread of networks and parallel and distributed computing ideas, methods shifted to “multi-agent” systems, game-theoretic approaches, swarms, and biomimicry of groups of animals. This course continues along these lines, but advancing to a focus on groups of humans interacting socially.

Course Objectives:

The course will involve (i) gaining an understanding of the functional operation of a variety of intelligent control techniques and their bio-psycho-social foundations, (ii) gaining an understanding of the modeling and operation of parallel and distributed algorithms over networks, (iii) the study of system and control-theoretic foundations (e.g., stability analysis), and (iv) use of the computer for simulation and evaluation. The objective will be to gain a "hands-on" working knowledge of basic ideas and techniques for modeling, analysis, and design of social, economic, and political systems. There are many technology applications of the ideas (e.g., to multi-agent systems), but the focus is not applications to technological systems, but the design, development, and use of embedded distributed technologies for control of complex social, economic, and political systems (i.e., control systems design).

Applications to Interacting Groups of Technologies: There are many applications of the ideas from this class to technology including: groups of autonomous / semi-autonomous robots/vehicles (land, water, air), groups of computers interacting over a wired or wireless network, distributed feedback control (e.g., with applications to temperature control, arrays of smart lights, and the smart grid), multi-agent systems (e.g., software), flexible manufacturing systems, etc.  If you are interested in such applications you should view this class as a theoretical biomimicry foundation for such methods (for more details on how to transfer ideas from this class to such applications see the publications at Passino’s web site given above).

Flexibility: Mathematical and Computational Tracks

There is significant flexibility embedded in this course, such as choice of your textbook (see above) and  final project (with permission of instructor) as it is explained below. Expectations will also be flexible. Some students may only do simulation for this course (at a minimum, simulation is required in the class, e.g., via Matlab/Netlogo); I call this the “computational track”. Others, such as PhD students in engineering doing research in the area, will be required to understand the underlying mathematics, including proofs; I call this the “mathematical track.” Overall, however, the expectations will be balanced to keep the work load roughly the same for all students (e.g., understanding a proof of convergence of social allocation or social motion is generally more difficult than simulating it, though not always). Otherwise, grading would not be fair.

Outline (topics not in order covered):

Grading: Homeworks, projects, midterm, book report (on your chosen textbook), and a final project.

Prerequisites: If you take the “computational track” all that is required is a willingness to work hard. For the “mathematical track” you need much more background (e.g., 3551, 5551, 5750, 5754) depending on the type of math track you take. I will be meeting individually with math track students to assign mathematical proofs that must be completed.