Stochastic Distribution Control aims at the control design for non-Gaussian stochastic systems so that its output probability density function (PDF) shape can be made to follow any specified PDF. The presenter originated the theory in 1996, and it has been applied to papermaking, chemical plants, and several industrial processes. It constitutes a power tool for generic modeling, data mining and decision making for systems uncertainties. In this talk, the basic algorithms will be given, and the contents represent the work done by the presenter when he was a chair professor at the University of Manchester, UK.
Professor Hong Wang (Fellow of IEEE, IET, InstMC and AAIA) received his MS and Ph.D. from the Huazhong University of Science and Technology, China, in 1984 and 1987, respectively. He was a research fellow at Salford, Brunel, and Southampton Universities before joining the University of Manchester Institute of Science and Technology (UMIST), UK, in 1992.
Wang was a chair professor in process control from 2002 to 2016, and he was the deputy head of the Paper Science Department and director of the UMIST Control Systems Centre (which was established in 1966 and is the birthplace of modern control theory) between 2004 and 2007. He was a member of the University Senate and General Assembly.
Between 2016 and 2018, he was with Pacific Northwest National Laboratory as a lab fellow and chief scientist and was the co-leader for the Control of Complex Systems. He joined Oak Ridge National Laboratory in January 2019 and is also an emeritus professor at the University of Manchester in the UK.
He was an associate editor for IEEE Transactions on Automatic Control, IEEE Transactions on Control Systems Technology, and IEEE Transactions on Automation Science and Engineering. He is also a member of three IFAC committees. Wang’s research focuses on stochastic distribution control, fault diagnosis and tolerant control, and uncertain systems optimization with applications to transportation, power grid and industrial systems.