Hong Wang

Hong Wang, Fellow of IET, InstMC, IEEE and AAIA, 2024 IEEE Fellow Cttee Member

Professor in Advanced Process Control

Personal profile



Publications listed at:


Career: Professor Hong Wang received the BSc, MEng and PhD degrees from Huainan University of Mining Engineering (淮南矿业学院, Huainan, AHUST) and Huazhong University of Science and Technology (华中工学院, Wuhan, HUST) in P R China 1982, 1984 and 1987, respectively. He then worked as a postdoc at Salford, Brunel and Southampton Universities (UK) between 1988 and 1992. He joined University of Manchester Institute of Science and Technology (UMIST, UK) in 1992 as a lecturer, and was then promoted to a Senior Lecturer in August 1997, to a Reader in August 1999, and to a full Chair Professor in Advanced Process Control in April, 2002.

Professor Hong Wang was the deputy head of the Paper Science Department between 1997 and 2002, and director of the Control Systems Centre between 2004 - 2007. He was the University senate member between 2007 and 2014, and member of general assembly between 2010 - 2014. He has been an Emeritus Professor with the University of Manchester since 2016.

International Standing: Professor Hong Wang is a member of 3 Technical Committees of the International Federation of Automatic Control. He was an associate editor of leading control journal IEEE Transactions on Automatic Control (2002 - 2004), an associate editor of IEEE Transactions on Control Systems Technology (2013 - 2019) and an associate editor of IEEE Transactions on Automation Science and Engineering (2013 - 2019), and editorial baord member for 7 international journals and has served as IPC member and conference chairman for many international conferences. He is the member of executive committee for UK Automatic Control Council and served twice as (Engineering and Physical Sciences Research Council (EPSRC) college member.

Research Contribution:

1) Stochastic Distribution Control Theory: Professor Wang originated the work on stochastic distribution control (SDC) in 1996, where the main purpose of control input design is to make the shape of the output probability density functions to follow a targeted function. This differs from the traditional stochastic control and optimization theory where only mean and variance are concerned. As such, Wang's stochastic distribution control theory has found a wide-spectrum of applications in data mining, signal processing, stochastic control, decision-making for complex and uncertain systems and finance systems, where risk analysis and management can be further advanced using probability density function modeling and control in SDC. For example, this theory also provides effective tools for solving decision-making problems for systems subjected to uncertainties, and how the impact from uncertainties can be minimized during the decision -making phase (https://onlinelibrary.wiley.com/doi/full/10.1002/oca.2755).  His book chapter titled "Decision-Making for Complex Systems Subjected to Uncertainties — A Probability Density Function Control Approach" has appeared in the SpringerLink book titled " Handbook of Reinforcement Learning and Control" in 2021 following his invited paper published at 2018 IEEE 14th International Conference on Control and Automation (ICCA) titled "Objective pdf-shaping-based economic dispatch for power systems with intermittent generation sources via simultaneous mean and variance minimization".

2) Fault Diagnosis and Tolerant Control: Professor Hong Wang has also made significant contributions on adaptive fault diagnosis and fault tolerant control for dynamic systems with successful applications in process industries.  He originated the theory of using adaptive control tuning mechanism to perform fast fault diagnosis for dynamic systems (https://ieeexplore.ieee.org/abstract/document/508919), and the collaborative fault tolerant control for complex dynamic systems composed of multi-agent subsystems.

3) {Max, +} Algebra and Human-in-Loop: In 2010, he originated the use of {max, +} algebra for the integrated modelling of complex systems (https://ieeexplore.ieee.org/abstract/document/6160654) and discovered the square root impact principle that provides consolidated foundation for the mathematical understanding of integration between industrialization and advanced information Technology (IT). Along this new research direction, he has developed novel quantitative description of human-in-loop systems using Brain to Computer Interface (BCI) techniques and has received best paper awards on this. Moreover, he acted as one of the themes leader for the first ever biggest mathematic initiative funded by the UK research council (EPSRC) titled "Centre for Interdisciplinary Computational and Dynamical Analysis" on Analysis of Adaptive Systems and Control - developing hybrid system modeling and control using tropical algebra. The initiative represents research collaboration among Manchester Math, Computing (where the world first computer was born) and Control systems.            

4) Nonlinear System Control and Robotics: Professor Wang developed a unified framework based control design for generic nonlinear systems with known dynamics, where it has been shown that a time-varying control with flexible structure can be used to control any nonlinear systems with the required adaptive tuning of control parameters (H. Wang, Y. Wang and P Kabore, Time-varying controller for known nonlinear dynamic systems with guaranteed stability, International Journal of Systems Sciences, Vol. 33, pp. 931 – 938, 2002).  In addition, he has also unified Lyapunov stability analysis with optimal control by taking the objective function as a Lyapunov function candidate - this allows simultaneous optimization with guaranteed stability for closed loop control systems.  The work has been applied to the design of robotic systems, where new tracking criteria (tracking error probability density functions and entropy) has been developed to obtain better tracking control of robotic systems with respect to their trajectories.

Publications: Professor Hong Wang is the leading author of three books and has also published over 300 papers in international journals and conferences. He has given 40+ invited and keynote talks at international conferences and has another detailed website on his publications at http://www.umist.ac.uk/paper.

Awards: Professor Wang received the outstanding oversea scholarship award from the Chinese National Sciences Foundation in 2002, and outstanding oversea academics award from the Chinese Academy of Sciences in 2004. He was one of the 120 outstanding oversea academics invited to attend the 50th birthday of the P R China in Beijing in 1999. He has also received the best paper award from Int Conf. Control 2006 and Jasbar Momorial Prize for his outstanding contribution in the Science and Technology Development for paper industries in 2006. He has also received several best paper awards between 2012 - 2016.

Off-work Activities: Professor Wang's other interests: Oil paintings and water colours (trained seven years), Radio-controlled airplanes/cars/boats and motor cycling, and kids education (Greater Manchester Chinese School).

Link to Control Systems Centre

Memberships of committees and professional bodies

  • IEEE
  • IET
  • Institute of Measurement and Control


BSc, MEng, PhD, FIET, FInstMC, FIEEE, Control Systems Centre

Research interests

1) Stochastic distribution control for non-Gaussian stochastic systems;

2) Fault detection, diagnosis and fault tolerant control for dynamic systems; 

3) Semi-physical modelling and neural networks for complex non-linear systems 

4) Applications of 1)-3) to papermaking systems and several other systems such as systems biology.

My group

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 7 - Affordable and Clean Energy

Research Beacons, Institutes and Platforms

  • Aerospace Research Institute
  • Digital Futures


  • Stochastic Distribution
  • Fault Diagnosis and Fault Tolerant Control
  • Complex Systems Modelling and Optimization


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