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Dr. S. Andrew Gadsden

Assistant ProfessorAndrew Gadsden

Department of Mechanical Engineering
University of Maryland, Baltimore County
1000 Hilltop Circle
Baltimore, MD 21250

Phone: (410) 455-3307
Office: ENGR225C


Postdoctoral experience, Centre for Mechatronics and Hybrid Technology, McMaster University, ON, Canada, 2011-2014
Ph.D., Mechanical Engineering (Thesis), McMaster University, ON, Canada, 2011
B.Eng.Mgt., Mechanical Engineering and Management, McMaster University, ON, Canada, 2006

Employment History

2014-present Assistant Professor, Mechanical Engineering, UMBC
2012-2014 Project Manager, Capital Engineering, Apotex, Inc., ON, Canada
2011-2014 Post-doctoral Fellow and Research Assistant, Centre for Mechatronics and Hybrid Technology, McMaster University

Honors and Awards

2016 Best Paper Award at the 2016 SPIE Sensing for Agriculture and Food Quality and Safety VIII Conference, Baltimore, Maryland, USA (SPIE 2016)
2015 UMBC Summer Faculty Fellowship
2012 Best Paper Award at the 2012 IEEE Transportation Electrification
Conference and Exposition, Dearborn, Michigan, USA (ITEC 2012)
2010-2012 Natural Sciences and Engineering Research Council of Canada (NSERC)
Industrial Research and Development Fellowship
2010-2011 NSERC Visiting Fellowship in Canadian Government Labs
2010 3rd Prize for Best Poster Presentation (Out of 25), Green Auto Powertrain Research Meeting, University of Waterloo, Ontario, Canada
2010 The Dean’s Award for Excellence in Communicating Graduate Research
2010 McMaster University Graduate Student Association Travel Award for International Conference (Information Fusion, Scotland, UK)
2009-2011 NSERC Postgraduate Scholarship
2009 McMaster Graduate Student Association Honour Society
2009-2010 Ontario Graduate Scholarship
2008-2009 The George and Alice Rivett Ontario Graduate Scholarship
2008-2009 Ontario Graduate Scholarship
2006-2011 McMaster University (Department of Mechanical Engineering) Graduate Scholarship

Research Interests

My research background includes a broad consideration of state and parameter estimation strategies, variable structure theory, control theory, fault detection and diagnosis, mechatronics, target tracking, cognitive systems, and neural networks. My current research interests may be categorized in three main areas: (1) advanced control systems and techniques; (2) state and parameter estimation theory; and (3) autonomous or cognitive systems. In the first area, we are targeting the development of new control methods that enable robustness to disturbances and uncertainties while providing accurate and stable system control. In the second area, we are creating and testing novel estimation strategies that provide improved performance in the presence of system and measurement nonlinearities. Finally, in the third area, we are developing techniques and methods in an attempt to push the boundary of autonomy in the area of cognitive systems. The target applications include, but are not limited to: robotics, unmanned vehicles, and mechatronic systems.

My laboratory, the Autonomous Systems Research Laboratory (ASRL), is located in room 235 of the Engineering Building. Consisting of nearly 1200 square feet of space, we have access to 3-D printers (plastics, nylon, carbon fibers), a 150W laser cutter (capable of cutting and engraving many types of plastics, foams, woods, metals), laser scanner (for 3D replication and scanning), LIDARs (for environmental mapping), high-resolution cameras, robotic arms, and many unmanned ground and aerial vehicles. Our research has been supported by the federal and state governments, as well as local industry. Additionally, we are in the process of developing a new university-level and high school-level STEM outreach program referred to as Take Flight Robotics. This program aims to promote STEM education to younger students by getting them excited with unmanned aerial vehicles.

Classes Taught at UMBC

ENME403 Automatic Controls (syllabus)
ENME482L Vibrations and Controls Laboratory (syllabus)
ENME605 Advanced Control Systems (syllabus)
ENME812 Advanced Estimation Theory (syllabus)