Control engineering is the mathematical modeling of dynamic systems and the design of controllers that cause the dynamic system to behave in a desired manner. Control engineering is being applied to allow advances in many fields including automotive, consumer products, process control, nuclear reactors, power systems, robotics, manufacturing and defense. Additionally, control engineering meets new challenges from the frontiers of science and technology such as information technology, biomedical technology, and nano technology. Control engineering areas in the Electrical and Computer Engineering Department include nonlinear control theory, control of smart materials, control of robotic systems, control of nanomanipuation systems, control of automotive systesms, ant adaptive optimal control and estimation.
Nonlinear Control: The controls group at MSU is known internationally for its research in nonlinear control. The group has made fundamental contributions to singular perturbations techniques for multiple-time-scale systems and to the design of output feedback control of nonlinear systems using high-gain observers. Research projects covered problems in robust and adaptive control of nonlinear systems and the nonlinear regulation problem. Applications included mechanical systems, electric drives, and smart materials.
Control of Smart Materials: Smart materials, such as piezoelectrics and electroactive polymers, have inherent sensing and actuation capabilities with broad applications in robotics, automotive and aerospace industry, and biomedical systems. Our research in this area is focused on understanding and modeling transduction mechanisms in novel smart materials from a systems perspective, and developing control theory and algorithms to address complex, nonlinear, hysteretic behaviors of these materials.
Control of Robotic Systems: Our goal is to develop theoretical foundations and implementation schemes for a number of robotic systems, ranging from swarms of biomimetic robots, to nanomanipulation systems, to telemedicine systems, to automotive manufacturing systems. The research activities include system modeling and analysis, sensor integration and data fusion, controller design, human and computer interaction, real-time computing architecture, and software development.
Automotive Control: We investigate closed-loop combustion control of internal combustion engine, by applying modern control theories to develop feed-forward and feedback strategies for optimized spark timing, EGR (Exhaust Gas Recirculation) rate, and engine value timing and lift. We also study the optimization and control of hybrid power train, which consists of many subsystems such as internal combustion engine, electric machine, transmission, and so on. Our goal is to develop real-time control strategies for the best fuel economy within a given level of emissions.
Adaptive Optimal Control and Estimation: the goal is to develop adaptive optimal control and estimation methods for partially or completely unknown system models. The methods emulate unsupervised learning in biological neural networks as well as information theoretic approaches for estimating and/or controlling engineering systems. We also focus on locking and synchronization among array of oscillators with applications to phased-antenna arrays and transceivers.