Brian Surgenor

About

Brian Surgenor is a leading figure in mechatronics and intelligent control systems, with a career focused on bridging the gap between theoretical control methods and practical robotic applications. His most impactful work centers on the integration of micro fuel cell technology into mobile robotics, where his pioneering 2006 study on converting a mechatronics course robot from lead-acid batteries to hydrogen PEM fuel cells (27 citations) demonstrated a viable pathway for clean, long-duration power in educational platforms. Surgenor has made significant contributions to pneumatic robot control, developing adaptive neural network compensators and fuzzy logic controllers that address the nonlinearities limiting precision tasks like grinding and polishing. His 2013 comparison of fuzzy and neural network adaptive methods (15 citations) is a key reference for researchers seeking robust position control solutions. Beyond research, Surgenor has had a profound educational impact through his mobile robot-based mechatronics course (9 citations), which has shaped how undergraduate students engage with sensor and actuator technologies. With over 100 combined citations across his top works, his legacy lies in making advanced control theory accessible and applicable, from hopping robots to industrial gantry systems.

Research Focus

Key Achievements

Top Papers

1. 1
   Design and evaluation of a micro-fuel-cell-based power system for a mobile robot

   27 citations · 2006
2. 2
   Evaluation of a micro fuel cell as applied to a mobile robot

   23 citations · 2006
3. 3
   Comparison of fuzzy and neural network adaptive methods for the position control of a pneumatic system

   15 citations · 2013
4. 4
   Jumping height control of an electrically actuated, one-legged hopping robot: modelling and simulation

   13 citations · 2002
5. 5
   Control of a pneumatic gantry robot for grinding: a neuro-fuzzy approach to PID tuning

   10 citations · 2005
6. 6
   Lessons Learned From A Mobile Robot Based Mechatronics Course

   9 citations · 2020
7. 7
   Force/Velocity Control of a Pneumatic Gantry Robot for Contour Tracking With Neural Network Compensation

   5 citations · 2008
8. 8
   Tracking with a fuzzy logic controller as applied to a pneumatic robot for polishing

   5 citations · 2017
9. 9
   A Novel Adaptive Neural Network Compensator as Applied to Position Control of a Pneumatic System

   5 citations · 2011
10. 10
    Hybrid Force/Velocity Control of a Pneumatic Gantry Robot for Contour Tracking: Tuning and Model Validation

    3 citations · 2007

Key Collaborators

Contact & Links