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Oklahoma State University

Intelligent Control Systems

Control

Overview

Control systems are a key enabling technology for the increase in functionality and safety of many critical applications such as transportation systems, manufacturing systems, medical devices, and networked embedded systems. The design of today's complex control systems is challenging since it requires a strong foundation in several disciplines including electrical, chemical, mechanical and aerospace engineering, as well as biology, computer science, and economics.

Research

With its traditional base of supporting statewide industry, it is not surprising that OSU has a strong interdisciplinary program in control systems engineering. Emphasizing neural networks and fuzzy logic, research programs are closely associated with a Master of Science in Control Systems Engineering degree program. Collaborations in the program are with Chemical Engineering, Industrial Engineering and Management, and Mechanical and Aerospace Engineering. Current research projects focus on predicting impending failures in complex interrelated structures, using assessment tools using emerging neural network and fuzzy logic technology. Additional work involves neural network based intelligent controllers capable of self-optimization, on-line adaptation and autonomous fault detection and controller reconfiguration.

Control

Courses

  • Intelligent Control
  • Control of Hybrid Systems
  • Adaptive Control
  • Digital Control
  • Nonlinear Aystems Analysis and Control
  • Optimal Control
  • Systems Theory
  • Neural Networks
  • System Identification
  • Estimation Theory
  • Optimization Applications

Research Labs

CEAT Interdisciplinary controls group
The Master of Science in Control Systems Engineering program
Intelligent Systems and Control Laboratory
Laboratory for Advanced Sensing Computation and Control

Faculty

Dr. Marty T. Hagan received his B.S. in electrical engineering from the University of Notre Dame in 1972, M.S. in information and computer science from the Georgia Institute of Technology in 1973 and his Ph.D. in electrical engineering from the University of Kansas in 1992. He has taught and conducted research in the area of system modeling and control for the last twenty years. Some of the application areas in his control research have been flight simulators, precision pointing systems, diesel engines, adaptive flight control and friction compensation. He has received industry grants from the National Science Foundation and Air Force Office of Scientific Research. For the last 10 years his research has focused on the use of neural networks for control, filtering and prediction.

Dr. Weihua Sheng received his Ph.D. in electrical and computer engineering from Michigan State University in 2002. His current research interests lie in the general area of intelligent sensing, computation, control and their applications. More specifically, his research directions include embedded intelligent sensing, robotized sensor networks, intelligent mechatronics and computational intelligence for manufacturing. He is a member of IEEE and has participated in organizing several IEEE conferences.

Dr. Gary Yen received his B.S. in electronics engineering from the National Taipei Institute of Technology in 1983, M.S. in electrical and computer engineering from Marquette University in 1983 and his Ph.D. in electrical and computer engineering from the University of Notre Dame in 1992. His research interests include intelligent system and control, predictive machinery diagnosis and multiple sensor data fusion. Dr. Yen has received continuous support from DoD, NASA and DoE Laboratories since 1992. He is an IEEE senior member and has served as an associate editor for the IEEE Transactions on Neural Networks and the IEEE Control Systems Magazine since 1995.