Department of Control Engineering
Control Engineering refers to the use of algorithms and feedback in engineered systems. Control engineering plays an essential role in the development of diversified technologies such as aerospace and transportation, information and networks, robotics and intelligent machines, biology and medicine, and materials and processing. The use of control is extremely broad and encompasses many different applications. These include control of electromechanical systems, control of electronic systems, and control of information and decision systems.
Contributions to the field of control engineering come to many disciplines, including pure and applied mathematics; aerospace, chemical, mechanical, and electrical engineering; operations research and economics; and the physical and biological sciences. The interaction with these different fields is an important part of the history and strength of the field.
The Control Engineering Department, established in 1980, engages in research and teaching in control systems and offers curricula leading to the BSc, MSc, and PhD degrees. The department provides an in-depth training in modern control engineering concepts, with special emphasis on System modelling, guidance and navigation, robotics, system identification and artificial intelligent systems. Students will find control engineering attractive due to its high technological relevance to an exciting, rapidly expanding and flourishing industry.
Many educational laboratories, including linear control system Lab, Robotic lab, Instrumentation Lab and Digital control Lab mainly intended for undergraduate students and/or research activities in graduate programs are equipped with the latest educational facilities and systems. The graduate students are admitted in three fields of control engineering as System engineering, robotic & AI, and guidance & navigation.
- Simulation techniques for hunt and hunter path planning
- Application of nonlinear Kalman filter in differential GPS
- Control of none homogenous multi agent robotic systems
- Multi robotic path planning by linguistic geometry
- Robust control of robot with flexible arm
- Design of optimal controller for suspension system of vehicles
- Intelligent path planning of AGV systems
- Analysis of stability and convergence of control systems based of Neural networks
- Modeling of human behavior during driving on stress condition using fuzzy set theory
- A project control model based on fuzzy decision making
- Optimal control of nonlinear system of biped robot
- Stability analysis of hybrid systems
The Control department provides a number of undergraduate and graduate courses generally in the areas of Applied Math and Control Theory.