• 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


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 spin-out company


Emerson Passivity-based 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


Journal Papers

  1. Chan, K. H., Dozal-Mejorada, 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)
  2. Li, K., Chan, K. H., Ydstie, B. E. and Bindlish, R. (2010), “Passivity-based Adaptive Inventory Control,” Journal of Process Control 20(10), pp. 1126–1132.
  3. Bao, J., Chan, K. H., Zhang, W. Z. and Lee, P. L. (2007), “An Experimental Pairing Method for Multi-loop Control Based on Passivity,” Journal of Process Control 17(10), pp. 787–798.
  4. Chan, K. H. and Bao, J. (2007), “Model Predictive Control of Hammerstein Systems with Multivariable Nonlinearities,” Industrial & Engineering Chemistry Research 46(1), pp. 168–180.
  5. 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. 659–670.
  6. 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. 329–342.

Conference Papers

  1. Chan, K. H., Ydstie, B. E. and Cheng, X. (2011), “Advanced Control Design for Boiler-Turbine Unit,” 2011 IEEE Multi-conference on Systems and Control, Denver, CO, USA.
  2. Li, K., Chan, K. H. and Ydstie, B. E. (2009), “Passivity-based Adaptive Inventory Control,” 48th IEEE Conference on Decision and Control, Shanghai, P.R. China.
  3. 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.
  4. 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 0-86869-110-0).
  5. Chan, K. H., Bao, J. and Whiten, W. J. (2004), “Control of Discrete-Time Hammerstein Systems Based on the Passivity Theorem,” Proceedings of the Fifth Asian Control Conference, Melbourne (ISBN: 0-73403-016-9, IEEE catalogue number 04EX904C), pp. 976–981.
  6. Chan, K. H., Bao, J. and Whiten, W. J. (2003), “Multi-loop 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



  • 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 part-time equivalent of a Bachelor program offered in the Faculty of Engineering
  • Faculty of Engineering Scholarship (1999), UAI 99 or above


  • 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. Highly-coupled 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 closed-loop system of each of the approaches. In the nonlinear compensator approach, the passivity of the system is recovered using an appropriate static nonlinearity. The non-passive 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 passivity-based stability conditions are used to obtain the invertible approximations of the subsystems and guarantee the stability of the nonlinear closed-loop 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.