The Ohio State University
Dept. Electrical and Computer Engineering

ECE 7858 Intelligent Control

Instructor: Prof. Kevin Passino 416 Dreese Laboratory,

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

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

Relevant Books (not required):

Background: 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 or other biological system) with fuzzy control, neural networks, planning systems, attentional systems, and with a heavy focus on learning/adaptive 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) and for adaptive 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 analytically 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.

Applications to Interacting Groups/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); however, applications to such technologies will not be considered. Yet, technology applications are a key interest, so long as they are focused on interventions to help groups, and extending the capabilities of human groups.

Course Objectives: Social systems modeling and anlaysis via gaining an understanding of modeling and qualitative analysis of networked and distributed dynamical systems, especially collective motion, agreement, choice, and allocation.

Outline (this will be updated as the Sp16 semester progresses):


Final Project: Read "Recent Developments in Role Theory" by Biddle: (i) Create a mathematical model of the elements of role theory, (ii) simulate it and show that it represents the notions of role theory, (ii) provide a mathematical characterizatoin of some property of the dynamics (e.g., convergence, stability, or boundedness) and discuss, with the support of simulations, why this property may hold. Due May 3, noon, via email to Prof Passino or by sliding it under his door at Rm 416 Dreese Labs. Due: Mon. May 2, noon. Slide under Prof Passino's door.

Optional Subjects (depending on time/chosen focus): Biological optimization (e.g., bacteria), distributed synchronization, game theory introduction, evolutionary game theory/evolutionary dynamics/replicator dynamics (ODE model), cooperative task processing, cooperative scheduling, distributed assignment/concensus, auctions, competitive and intelligent foraging.  

Grading: Homeworks and a final project. Weighting on these, for determining the final grade, will be determined at the end of class as it depends on the difficulty level of all assignments.