Research

Alleyne Research Group Overview Poster

 

Energy Management Systems

Energy management systems are becoming an important area of research as non-renewable energy resources are being consumed at an unsustainable rate.  While the traditional method of improving system efficiency of a range of thermal systems, from combustion engines to vapor compression systems, has been through advanced hardware design, we are learning that advanced control techniques offer a unique opportunity to significantly improve not only the efficiency, but also the performance, of these systems. The research projects presented here focus on a range of energy domains, ranging from large building heating and cooling systems to energy systems in air vehicles.

Vehicle System Dynamics and Control

Vehicle systems research represents a very diverse and continuously evolving field of study.  The research projects presented here focus on improving the efficiency and effectiveness of vehicle systems through the use of controls.  They span a broad range of applications from small urban vehicles to large farming equipment.  These applications also give rise to a wide range of controls challenges such as adaptive control, optimal control, and control of systems with hybrid dynamics.  Improvements to vehicle systems are being made through optimization of power generation/distribution and automation of vehicle task, such as steering and actuation.  These advancements will address the need for more efficient, safe, and productive vehicles on a fundamental level.

Micro/Nano Scale Manufacturing Control

Manufacturing is necessary for any society.  In the Alleyne Research Group, we aim to provide control and automation solutions to advanced manufacturing processes at the micro and nano-scale.  With improved positioning of manufacturing toolbits comes advanced precision in the manufacturing process.  We study a specific form of control betterment methods termed iterative learning control (ILC) in which control performance is improved through process repetition.  By learning from previous repetitions of the process, we can synthesize supplementary feedforward signals that counteract disturbances and nonlinearities that are detrimental to process accuracy. We have applied these control schemes on microscale positioning systems, micro-extrusion systems in a fabrication process termed micro-Robotic Deposition, and a nanoscale fabrication process termed electrohydrodynamic jet printing.