My Expertise
Mechatronic systems, robotics, control of heavy goods vehicles, decarbonisation of transport, rail vehicle modelling and control, applied machine learning, deep learning for vehicle safety applications, machine vision for property estimation, electric vehicle control.
Fields of Research (FoR)
Simulation, modelling, and programming of mechatronics systems, Control engineering, mechatronics and robotics, Control engineering, Automotive mechatronics and autonomous systems, Mechatronics hardware design and architecture, Rail transportation and freight services, Hybrid and electric vehicles and powertrainsSEO tags
Biography
Will is a Senior Lecturer in Mechatronics and Robotics in the School of Mechanical and Manufacturing Engineering, Faculty of Engineering, UNSW Sydney. He received his PhD from Cambridge University, then went on to work for Mitsubishi Heavy Industries in Japan before returning to academia to work at Loughborough University and now working at UNSW Sydney.
Will is interested in using control engineering to help reduce emissions from transport....view more
Will is a Senior Lecturer in Mechatronics and Robotics in the School of Mechanical and Manufacturing Engineering, Faculty of Engineering, UNSW Sydney. He received his PhD from Cambridge University, then went on to work for Mitsubishi Heavy Industries in Japan before returning to academia to work at Loughborough University and now working at UNSW Sydney.
Will is interested in using control engineering to help reduce emissions from transport. This has spanned work on modelling and building hydraulic hybrid heavy goods vehicles, using advanced control techniques to reduce the emissions of rail vehicles, optimising the fuel used by plug-in hybrid road vehicles, applying optimisation techniques to determine the best way to electrify railways and using resonant control to reduce the torque ripple produced by permanent magnet synchronous machines (PMSMs - electric motors).
He has also worked on several applications of machine learning (ML) and artificial intelligence (AI) to real-world engineering problems: using machine learning to deduce parameters of vehicles while in motion; developing neural networks to determine a vehicle's speed using machine vision; determining the properties of the road surface ahead of a vehicle using machine learning and machine vision.
For more details, visit his website: willmidgley.com or email him.
If you would like to do a PhD with Will, please get in touch with him.
My Grants
- Don't Forget the Mortar! A New Approach to Engineering Education - £65,000 - Royal Academy of Engineering (UK) - 2022-23
- Optimisation of Intermittent Electrification of Rail Transport for Near-Term Decarbonisation - £37,000 - DTE Network+ (UK) - 2021-22
- Tyre-Road Friction Estimation using Maximum Entropy - £24,000 - EPSRC (UK) - 2021-22
- Decarbonising High-Speed Bi-Mode Railway Vehicles through Optimal Power Control - £158,000 - RSSB (UK) - 2019-20
My Awards
- IMechE T A Stewart-Dyer Prize/Frederick Harvey Trevithick Prize for “the most meritorious paper on the subject of railway engineering”, IMechE, (2022)
- Fellowship of the Higher Education Academy (now AdvanceHE) (2020-)
- Best Poster Award, Intelligent Fluid Power Transmission and Control, University of Bath (2019)
- 2012 SAGE Highly Commended Paper for “Comparison of regenerative braking technologies for heavy goods vehicles in urban environments” (2013)
My Research Supervision
Supervision keywords
Areas of supervision
Applied control; applied mechatronics; applied machine learning
Currently supervising
External Advisor:
Mohammad Otoofi (Loughborough University, UK) - Computer vision for road surface identification
ORCID as entered in ROS
https://orcid.org/0000-0003-3786-1691