
Kwong Ho (Richard) Chan

Carnegie Mellon University

Department of Chemical Engineering

5000 Forbes Avenue

Doherty Hall, Room 3112

Pittsburgh, PA 15213

kwonghoc@andrew.cmu.edu
Education
2003 — 2007

Ph.D., Chemical Engineering, The University of New South Wales


Thesis Title: Identification and Control of Nonlinear Processes with Static Nonlinearities

1999 — 2003

B.S., Chemical Engineering, The University of New South Wales

Industrial and Research Experience
2007 — 2014

Process Engineer, Industrial Learning Systems, Inc. (iLS), a Carnegie Mellon University spinout company

Projects
Emerson

Passivitybased adaptive inventory control for superheater


Analysis of decentralised control of power plants


Modelling of power plant units


System identification algorithm and software

ExxonMobil

Adaptive model predictive control for petroleum industries

Hemlock

Pressure control design

NSF SBIR Phase I

Principal Investigator: Continuous Production of Crystalline Silicon Sheet for Solar Cell

Cheswick Power Plant

Design controller on SCR Unit

KEPRI

Implement and test power plant controller on simulator

Publications
Journal Papers
 Chan, K. H., DozalMejorada, E. J., Cheng, X., Kephart, R. and Ydstie, B. E. (2013), Predictive Control with Adaptive Model Maintenance: Application to Power Plants, Computers & Chemical Engineering (submitted)
 Li, K., Chan, K. H., Ydstie, B. E. and Bindlish, R. (2010), Passivitybased Adaptive Inventory Control, Journal of Process Control 20(10), pp. 11261132.
 Bao, J., Chan, K. H., Zhang, W. Z. and Lee, P. L. (2007), An Experimental Pairing Method for Multiloop Control Based on Passivity, Journal of Process Control 17(10), pp. 787798.
 Chan, K. H. and Bao, J. (2007), Model Predictive Control of Hammerstein Systems with Multivariable Nonlinearities, Industrial & Engineering Chemistry Research 46(1), pp. 168180.
 Chan, K. H., Bao, J. and Whiten, W. J. (2006), Identification of MIMO Hammerstein Systems Using Cardinal Spline Functions, Journal of Process Control 16(7), pp. 659670.
 Chan, K. H., Bao, J. and Whiten, W. J. (2005), A New Approach to Control of MIMO Processes with Static Nonlinearities Using an Extended IMC Framework, Computers & Chemical Engineering 30(2) pp. 329342.
Conference Papers
 Chan, K. H., Ydstie, B. E. and Cheng, X. (2011), Advanced Control Design for BoilerTurbine Unit, 2011 IEEE Multiconference on Systems and Control, Denver, CO, USA.
 Li, K., Chan, K. H. and Ydstie, B. E. (2009), Passivitybased Adaptive Inventory Control, 48th IEEE Conference on Decision and Control, Shanghai, P.R. China.
 Li, K., Chan, K. H. and Ydstie, B. E. (2008), Adaptive Inventory Control of Superheater, 2008 IEEE Power Engineering Society General Meeting, Pittsburgh, PA, USA.
 Chan, K. H., Xu, S. C. and Bao, J. (2006), A New Experimental Procedure for Nonlinear Process Modelling, Proceedings of Chemeca 2006, Auckland (Paper 425, CDROM, ISBN 0868691100).
 Chan, K. H., Bao, J. and Whiten, W. J. (2004), Control of DiscreteTime Hammerstein Systems Based on the Passivity Theorem, Proceedings of the Fifth Asian Control Conference, Melbourne (ISBN: 0734030169, IEEE catalogue number 04EX904C), pp. 976981.
 Chan, K. H., Bao, J. and Whiten, W. J. (2003), Multiloop Digital PI Control Design on the Basis of the Passivity Theorem, Proceedings of Chemeca 2003, Adelaide (Paper 138, CDROM).
Teaching Assistant
2003 — 2006

Process Control (CEIC3070) and Laboratory Automation Science (CEIC4070), The University of New South Wales

Awards
Academic
 Australian Postgraduate Award (2003 2006)
 Best Poster (finalist) in the 5th Asian Control Conference at Melbourne (2004)
 1st Class Honour in Bachelor of Chemical Engineering (2003), Average 75 or above
 The John Fraser Memorial Award (2000), Best performance in Year 1 or parttime equivalent of a Bachelor program offered in the Faculty of Engineering
 Faculty of Engineering Scholarship (1999), UAI 99 or above
NonAcademic
 Sports Administrator Recognition Award (Special Mentioned, 2005), The University of New South Wales Sports Association
 Club Administrator of the Year (Nominee, 2005), Australian University Sports East
 Australian Mathematical Olympiad (Distinction, 1998)
Research Interests
 System Identification
 Process Modelling
 Nonlinear Control
 Robust Adaptive Control
 Optimisation Theory
 Model Predictive Control
Abstract of PhD Thesis
In this project, the multivariable nonlinear processes are approximated using a model with a static nonlinearity and a linear dynamics. In particular, the Hammerstein model structure, where the nonlinearity is on the input, is used. Cardinal spline functions are used to identify the multivariable input nonlinearity. Highlycoupled nonlinearity can also be identified due to flexibility and versatility of cardinal spline functions. An approach that can be used to identify both the nonlinearity and linear dynamics in a single step has been developed. The condition of persistent excitation has also been derived.
Nonlinear control design approaches for the above models are then developed in this thesis based on: (1) a nonlinear compensator; (2) the extended internal model control (IMC); and (3) the model predictive control (MPC) framework. The concept of passivity is used to guarantee the stability of the closedloop system of each of the approaches. In the nonlinear compensator approach, the passivity of the system is recovered using an appropriate static nonlinearity. The nonpassive linear system is passified using a feedforward system, so that the passified overall system can be stabilised by a passive linear controller with the nonlinear compensator. In the extended IMC approach, dynamic inverses are used for both the input nonlinearity and linear dynamics. The concept of passive systems and the passivitybased stability conditions are used to obtain the invertible approximations of the subsystems and guarantee the stability of the nonlinear closedloop system. In the MPC approach, a numerical inverse is implemented. The condition for which the numerical inversion is guaranteed to converge is derived. Based on these conditions, the input space in which the numerical inverse can be obtained is identified. This constitutes new constraints on the input space, in addition to the physical input constraints. The total input constraints are transformed into linear input constraints using polytopic descriptions and incorporated in the MPC design.