Syllabus
The Ohio State University
Dept. Electrical Engineering
EE 858 Intelligent Control
Spring 2003
Instructor: Prof. Kevin Passino 416 Dreese Laboratory, Phone: 292-5716, passino@ee.eng.ohio-state.edu
Office Hours: Stop by any time (but try to set an appointment)
Course Book/Notes: This course is taught out of the book: K. Passino, Biomimicry for Optimization, Control, and Automation, the web site of which you can go to by clicking here.
Relevant Texts (not needed for this course but both are available for a free download by clicking here):
- Kevin M. Passino and Stephen Yurkovich, Fuzzy Control, Addison Wesley Longman, Menlo Park, CA, 1998.
- Antsaklis P.J., Passino K.M., eds., An Introduction to Intelligent and Autonomous Control, Kluwer Academic Publishers, Norwell, MA, 1993.
Course Objectives:
The course will involve (i) gaining an understanding of the functional operation of a variety of intelligent control techniques and their bio-foundations, (ii) the study of control-theoretic foundations (e.g., robustness), (iii) learning analytical approaches to study properties (especially stability analysis), and (iv) use of the computer for simulation and evaluation. The objective will be to gain a "hands-on" working knowledge of several of the main techniques of intelligent control and an introduction to some promising research directions.
Outline: Topics to be covered include:
- Week 1: Introduction (control foundations, biomimicry), Instinctual neural control (multilayer perceptron, radial basis function neural network, design example, stability analysis)
- Week 2: Fuzzy and expert control (standard, Takagi-Sugeno, mathematical chararacterizations, design example)
- Week 3: Planning systems (autonomous vehicle guidance for obstacle avoidance, model predictive control), Attentional systems (attentional strategies for predators/prey)
- Week 4: Learning and function approximation (function approximation problem), adaptive control introduction
- Week 5: Learning/adaptation (training neural networks and fuzzy systems with least squares and gradient methods), stable fuzzy/neural adaptive control
- Week 6: Evolutionary methods (genetic algorithm, evolutionary design)
- Week 7: Foraging, bacteria and connections to optimization and control
- Week 8: Foraging, bees and connections to optimization
- Week 9: Swarm stability (cohesion, foraging)
- Week 10: Competitive foraging games, coordinated vehicular guidance applications
Grading: Homeworks, Projects, Final examination
- Graduate standing
- EE 551 Feedback Control Systems (classical control) or an equivalent course
- EE 750 Linear Systems
- EE 754 Nonlinear Control Systems is highly recommended (at least as a co-requisite)
- Or permission from the instructor
Scheduling: This course is offered in Spring Quarter of odd numbered years
Last updated: 2/14/03