Byron Mason

a headshot of a man
School: School of Science & Technology
Department: Robotics and Mechatronics Engineering
Position: Senior Lecturer in Robotics and Mechatronics Engineering
Location: RMIT Saigon South
ORCID iD: 0000-0002-9530-5020

Byron Mason completed his PhD degree in mechatronic system simulation for controls validation in 2009. Before joining RMIT Vietnam he held academic positions at two different UK universities, most recently, as Associate Professor (Loughborough University).

His research and teaching interests are in the areas of machine learning and system identification with a particular interest in online and self-learning systems. He has significant experience in collaborative research leadership, having received external funding of over US$16 million to date.

  • 2020: Professional Certificate in Machine Learning Applications (London School of Economics)
  • 2010: Postgraduate Certificate in Higher Education Practice (University of Bradford)
  • 2009: PhD (University of Bradford)

  • Robotics and mechatronic systems
  • Mechatronic system simulation
  • Mathematical modelling
  • Optimization

  • Machine learning
  • Real time learning systems
  • Nonlinear system identification
  • Artificial intelligence
  • Adaptive control systems (including model predictive control and offline and real time mechatronic system simulation)

Key Publications / Creative Works Key publications (2019 – present).

  • W. Gu, J. Lan, and B. Mason, ‘Online neuro-fuzzy model learning of dynamic systems with measurement noise’, Nonlinear Dyn, vol. 112, no. 7, pp. 5525–5540, Apr. 2024, doi: 10.1007/s11071-024-09360-x.
  • Z. Yang, B. Mason, E. Winward, and M. Cary, ‘Two-Dimensional Transient Cycle Decomposition and Reduction (CDR) for Data Driven Nonlinear Dynamic System Modeling’, IEEE Access, vol. 12, pp. 37093–37102, 2024, doi: 10.1109/ACCESS.2024.3374892.
  • P. Saiteja, B. Ashok, B. Mason, and P. S. Kumar, ‘Assessment of Adaptive Self-Learning-Based BLDC Motor Energy Management Controller in Electric Vehicles Under Real-World Driving Conditions for Performance Characteristics’, IEEE Access, vol. 12, pp. 40325–40349, 2024, doi: 10.1109/ACCESS.2024.3375753.
  • Z. Yang et al., ‘Estimation of Piston Surface Temperature During Engine Transient Operation for Emissions Reduction’, J Eng Gas Turbine Power, pp. 1–11, Mar. 2024, doi: 10.1115/1.4065061.
  • W. Gu, J. Lan, and B. Mason, ‘Filter-based Online Neuro-Fuzzy Model Learning using Noisy Measurements’, in 2023 International Joint Conference on Neural Networks (IJCNN), IEEE, Jun. 2023, pp. 1–6. doi: 10.1109/IJCNN54540.2023.10191084.
  • J. Yang et al., ‘A Less-Disturbed Ecological Driving Strategy for Connected and Automated Vehicles’, IEEE Transactions on Intelligent Vehicles, vol. 8, no. 1, pp. 413–424, Jan. 2023, doi: 10.1109/TIV.2021.3112499.
  • P. Saiteja, B. Ashok, B. Mason, and S. Krishna, ‘Development of Efficient Energy Management Strategy to Mitigate Speed and Torque Ripples in SR Motor Through Adaptive Supervisory Self-Learning Technique for Electric Vehicles’, IEEE Access, vol. 11, pp. 96460–96484, 2023, doi: 10.1109/ACCESS.2023.3311851.
  • A. Gurusamy, B. Ashok, and B. Mason, ‘Prediction of Electric Vehicle Driving Range and Performance Characteristics: A Review on Analytical Modeling Strategies With Its Influential Factors and Improvisation Techniques’, IEEE Access, vol. 11, pp. 131521–131548, 2023, doi: 10.1109/ACCESS.2023.3334620.
  • S. Smith, J. Knowles, B. Mason, and S. Biggs, ‘Bifurcation analysis of a rear axle tramp car model’, Nonlinear Dyn, vol. 111, no. 17, pp. 15873–15890, Sep. 2023, doi: 10.1007/s11071-023-08678-2.
  • S. Petrovich, K. Ebrahimi, B. Mason, and A. Watson, ‘Modeling Transient Control of a Turbogenerator on a Drive Cycle’, SAE Int J Adv Curr Pract Mobil, vol. 4, no. 6, pp. 2022-01–0415, Mar. 2022, doi: 10.4271/2022-01-0415.
  • S. Tajdaran, F. Bonatesta, B. Mason, and D. Morrey, ‘Simulation of Traffic-Born Pollutant Dispersion and Personal Exposure Using High-Resolution Computational Fluid Dynamics’, Environments, vol. 9, no. 6, p. 67, May 2022, doi: 10.3390/environments9060067.
  • B. Ashok et al., ‘Towards Safer and Smarter Design for Lithium-Ion-Battery-Powered Electric Vehicles: A Comprehensive Review on Control Strategy Architecture of Battery Management System’, Energies (Basel), vol. 15, no. 12, p. 4227, Jun. 2022, doi: 10.3390/en15124227.
  • E. Winward, Z. Yang, B. Mason, and M. Cary, ‘Excitation Signal Design for Generating Optimal Training Data for Complex Dynamic Systems’, IEEE Access, vol. 10, 2022, doi: 10.1109/ACCESS.2021.3138166.
  • Z. Yang, B. Mason, W. Gu, E. Winward, and J. Knowles, ‘Computationally Efficient Nonlinear Model Predictive Control’, in 2022 8th International Conference on Control, Decision and Information Technologies, CoDIT 2022, 2022. doi: 10.1109/CoDIT55151.2022.9803958.
  • S. Smith, J. Knowles, B. Mason, and S. Biggs, ‘A Bifurcation Analysis and Sensitivity Study of Brake Creep Groan’, International Journal of Bifurcation and Chaos, vol. 31, no. 16, Dec. 2021, doi: 10.1142/S0218127421502552.
  • J. Parnell, M. Peckham, B. Mason, and E. Winward, ‘RDE vehicle emissions improvements assessed on a London route’, in Powertrain Systems for Net-Zero Transport, London: CRC Press, 2021, pp. 197–211. doi: 10.1201/9781003219217-12.
  • S. Smith, J. Knowles, and B. Mason, ‘Numerical continuation applied to internal combustion engine models’, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, vol. 234, no. 14, 2020, doi: 10.1177/0954407020928665.
  • D. Zhao, R. Stobart, and B. Mason, ‘Real-Time Energy Management of the Electric Turbocharger Based on Explicit Model Predictive Control’, IEEE Transactions on Industrial Electronics, vol. 67, no. 4, pp. 3126–3137, Apr. 2020, doi: 10.1109/TIE.2019.2910033.
  • S. Le Corre, T. G. Childs, M. Cary, B. Mason, and B. Coates, ‘Three Pattern Recognition Approaches to the Reduction of Vehicle Driving Cycles’, in ASME 2020 Internal Combustion Engine Division Fall Technical Conference, American Society of Mechanical Engineers, Nov. 2020. doi: 10.1115/ICEF2020-2955.
  • D. Zhao, R. Stobart, and B. Mason, ‘Optimising the Energy Efficiency and Transient Response of Diesel Engines through an Electric Turbocharger’, in 2019 American Control Conference (ACC), IEEE, Jul. 2019, pp. 298–303. doi: 10.23919/ACC.2019.8815017.
  • W. Gu, D. Zhao, and B. Mason, ‘Real-time Modelling and Parallel Optimisation of a Gasoline Direct Injection Engine’, in 2019 American Control Conference (ACC), IEEE, Jul. 2019, pp. 5544–5549. doi: 10.23919/ACC.2019.8814359.
  • Z. Yang, E. Winward, B. Mason, S. Le Corre, T. Childs, and A. Shahzad, ‘Nonlinear Model Predictive Control of a Variable Valve Timing System in a Turbocharged Spark Ignition Engine’, in 2019 American Control Conference (ACC), IEEE, Jul. 2019, pp. 4344–4349. doi: 10.23919/ACC.2019.8814687.
  • W. Gu, D. Zhao, and B. Mason, ‘A Review of Intelligent Road Preview Methods for Energy Management of Hybrid Vehicles’, IFAC-PapersOnLine, vol. 52, no. 5, pp. 654–660, 2019, doi: 10.1016/j.ifacol.2019.09.104.
  • M. Ghomashi, M. Cary, B. Mason, T. Childs, and K. Ebrahimi, ‘A Non-linear approach to dynamic torque modelling for expedited engine characterisation, control and calibration’, IFAC-PapersOnLine, vol. 52, no. 5, pp. 661–666, 2019, doi: 10.1016/j.ifacol.2019.09.105.
  • S. D. Le Corre et al., ‘Application of Multi-Objective Optimization Techniques for Improved Emissions and Fuel Economy over Transient Manoeuvres’, in SAE Technical Papers, Apr. 2019. doi: 10.4271/2019-01-1177.
  • S. Smith, J. Knowles, and B. Mason, ‘A Bifurcation Analysis of an Open Loop Internal Combustion Engine’, in SAE Technical Papers, Apr. 2019. doi: 10.4271/2019-01-0194.
  • D. Zhao, W. Gu, and B. Mason, ‘Real Time Energy Management of Electrically Turbocharged Engines Based on Model Learning’, in SAE Technical Papers, Apr. 2019. doi: 10.4271/2019-01-1056.
  • M. Duckhouse, M. Peckham, B. Mason, E. Winward, and M. Hammond, ‘In-Cylinder CO2 Sampling Using Skip-Firing Method’, J Eng Gas Turbine Power, vol. 141, no. 8, Aug. 2019, doi: 10.1115/1.4043396. 

Funding obtained (2019 – present)

  • January 2024: Optimization of battery electric vehicle powertrain systems (Chief Investigator), InnovateUK, £248k.
  • October 2023: Online adaptive optimization and model predictive control of thermal energy systems for improving BEV efficiency (Chief Investigator), Engineering and Physical Sciences Research Council, £139k.
  • December 2021: EGR cooler fouling modelling and Bayesian optimization for model parameter identification (Chief Investigator), Industry funding, £453k
  • June 2021: Machine learning applied to automotive battery system characterization for improving performance and reducing development time (Chief Investigator), InnovateUK, £197k.
  • January 2021: Electrical machine advanced testing facility upgrade (Chief Investigator), Engineering and Physical Sciences Research Council, £675k.
  • September 2019: Machine learning and advanced powertrain calibration optimization (Chief Investigator), Industry funding, £480k

Research degrees supervised (2019 – present)

  • PhD (completed 2024): Analysis of a Chaotic System Using Synergistic Mutual Information and Simplified Probability Functions.
  • PhD (completed 2022): Model predictive control for battery electric powertrains. 
  • PhD (completed 2022): Neuro fuzzy modelling and predictive control of automotive powertrains.
  • PhD (completed 2022): Bifurcation analysis for vehicle controls development. 
  • PhD (completed 2022): Adaptive model predictive control of powertrain systems with noisy measurement.
  • PhD (completed 2021): The development and evaluation of an Industry 4.0 logistics traceability system for end-of-life data sensitive equipment.
  • PhD (completed 2021): A novel design approach to embedded intelligent systems. 
  • PhD (completed 2020): Dynamic systems optimisation.
  • PhD (completed 2019): Engine testing temperature transient measurement and modelling. 
  • PhD (completed 2019): Virtual sensor for stress monitoring shafts using distributed lumped model. 

  • 2020: Nomination for Global Henry Ford Technical Award 
  • 2017: University Research Fellowship

Industry Experience Collaborated over many years with several multinational organizations in research to improve products and engineering efficiency. Partners have included Ford, Jaguar Land Rover, AVL, Caterpillar, HORIBA and many others.

  • 2022 - 2024: Associate Professor in Advanced Propulsion (Loughborough University, UK)
  • 2015 - 2022: Senior Lecturer in Advanced Propulsion (Loughborough University, UK)
  • 2014 - 2015: Senior Lecturer in Mechanical Engineering (University of Bradford, UK)
  • 2007 - 2015: Lecturer in Automotive Engineering (University of Bradford, UK)