Professor Morten Hovd
Dr. Ing. Process Control, B.Sc. (Hons) Natural Gas EngineeringDepartment of Engineering Cybernetics, Norwegian University of Science and Technology N-7491 Trondheim, Norway e-mail: morten.hovd+++itk.ntnu.no Phone: +47 73591426 Fax: +47 73594599
TTK 4210 Advanced Control of Industrial Processes
My research interests cover a range of topics of relevance to the design, operation and maintenance of industrial control systems. For applied research, focus has been on the chemical processing industries. However, I am also involved with the Norwegian Smart Grid Center, and will work more with applications in electrical power transmission and distribution in the future.
Industrial control systems are large-scale systems, where the numbers of sensors and actuators can easily reach into the hundreds or even thousands. It is therefore necessary to impose structure on the control system, to break system design (and operation) into smaller, more manageable parts. Work in this area has focused on the use and implications of the Relative Gain Array (RGA), and the related measures Performance RGA (PRGA) and the Closed Loop Disturbance Gain (CLDG). My most important work in this area include
Hovd, M. and Skogestad, S. (1992). Simple Frequency-dependent Tools for Control System Analysis, Structure Selection and Design. Automatica, Vol. 28, No. 5, pp. 989-996.
Hovd, M. and Skogestad, S. (1994). Pairing Criteria for Decentralized Control of Unstable Plants. Industrial & Engineering Chemistry Research, Vol. 33, No. 9, pp. 2134-2139.
The RGA, in its many guises, continue to be an active research area. Many of the more recent additions to the literature in this area would be superfluous if authors would study the older literature and properly grasp the difference between the steady state and dynamic RGA.
The most basic performance requirement is that the system should be able to keep the system stable, and stabilize unstable systems. It is well known that for stabilization, feedback is required, and it is a commonly held opinion that feedforward has no role in stabilization. Recent research shows that when input constraints are present (which in practice always is the case), a properly designed feedforward can be crucial in achieving stability:
Hovd, M. and Bitmead, R. R. (2009). Feedforward for stabilization. IFAC Symposium ADCHEM, Istanbul, Turkey, July 2009.
An updated and extended version was recently submitted to the Journal of Process Control.
Model Predictive Control (MPC) is by far the most common advanced controller type in the process industries, and its ability to handle constraints both in inputs and outputs is an important reason for its popularity. Work in this area has included
Hovd, M., Michaelsen, R., and Montin, T. (1997). Model Predictive Control of a Crude Oil Distillation Column. Computers and Chemical Engineering, Vol. 21, Suppl. pp. S893-S897.
MPC problem formulation:
Hovd, M. and Braatz, R. D. (2001). Handling state and output constraints in MPC using time-dependent weights. American Control Conference, Arlington, Virginia, USA
Hovd, M. (2011). Multi-level Programming for Designing Penalty Functions for MPC Controllers. Accepted for publication at 18th IFAC World Congress, Milan, Italy, August-September 2011.
Introducing advanced functionality into MPC, for identification and state estimation:
Hovd, M. and Bitmead, R. R. (2005). Interaction between control and state estimation in nonlinear MPC. Modeling, Identification and Control, Vol. 26, No. 3, pp. 165 – 174.
Marafioti, G., Bitmead, R.R., and Hovd, M. (2011). Persistently Exciting Model Predictive Control. Submitted to International Journal of Adaptive Control and Signal Processing.
Hovd, M., Scibilia, F., Maciejowski, J. and Olaru, S. (2009). Verifying Stability of Approximate Explicit MPC. 48th IEEE Conference on Decision and Control, Shanghai, December 2009.
Scibilia, F., Olaru, S. and Hovd, M. (2011). On feasible sets for MPC and their approximations. Automatica, Vol 47, No. 1, pp. 133-139.
Conventional MPC and explicit MPC both have their shortcomings, leading to an interest in simplified control approaches that retain MPC’s ability to handle constraints. Recent work in this vein includes
Nguyen, H. N., Gutman, P.-O., Olaru, S. and Hovd, M. (2011). An interpolation approach for robust constrained output feedback. Accepted for publication at 50th IEEE Conference on Decision and Control, Orlando, Florida, December 2011.
MPC leads to a hybrid system with piecewise affine closed loop dynamics, and for simplified/approximate explicit MPC closed loop stability is often not ensured by the design formulation. For such systems, alternative approaches are necessary to verify closed loop stability. Work in this area has centered on improved LMI formulations for stability verification:
Hovd, M. and Olaru, S. (2010). Piecewise quadratic Lyapunov functions for stability verification of approximate explicit MPC. Modeling, Identification and Control, Vol 31, No. 2, pp. 45-53.
Hovd, M. and Olaru, S. (2011). Relaxing PWQ Lyapunov stability criteria for PWA systems. Submitted to Automatica.
In some cases, plant structure can be utilized in controller design, effectively turning a large, complex design problem into a series of independent, smaller problems.
Hovd, M., Braatz, R. D. and Skogestad, S. (1994). SVD Controllers for H2-,H∞- and m-optimal Control. Automatica, Vol. 33, No. 3, pp. 433-439.
Hovd, M. and Skogestad, S. (1994). Control of Symmetrically Interconnected Plants. Automatica. Vol. 30, No. 6, pp. 957-973.
Additional details may be found in my PhD thesis (1992). Closely related ideas have since been applied to cross-directional control in paper making.
Work in this area has generally involved application to specific problem areas. Publications include
Jakobsen, S. R., Hestetun, K. Hovd, M. and Solberg, I. (2001). Estimating alumina concentration distribution in aluminium electrolysis cells. 10th IFAC Symposium on Automation in Mining, Mineral and Metals Processing, Tokyo, Japan.
Hestetun, K. and Hovd, M. (2005). Detecting abnormal feed rates in aluminium electrolysis using the extended Kalman filter. IFAC World Congress, Prague, Czech Republic, July 2005.
Marafioti, G., Olaru, S. and Hovd, M. (2009). State Estimation in Nonlinear Model Predictive Control, Unscented Kalman Filter Advantages. In Nonlinear Model Predictive Control Towards New Challenging Applications, Lecture Notes in Control and Information Sciences, Vol 384, Springer, pp. 305 – 313.
For a full publication list please follow the link at the top of this page.
Morten Hammer (2004). Dynamic Simulation of a Natural Gas Liquefaction Plant. (Co-supervisor, principal supervisor prof. Geir Owren, Department of Energy and Process Technology).
Kristin Hestetun (2009). Use of Data from Anode Current Distribution for State and Parameter Estimation and Fault Detection in an Aluminium Prebake Electrolysis Cell.
Giancarlo Marafioti. (2010). Enhanced Model Predictive Control: Dual Control Approach and State Estimation Issues.
Francesco Scibilia. (2010). Explicit Model Predictive Control: Solutions via Computational Geometry.
Editor: Professor Morten Hovd, Contact address: morten.hovd+++itk.ntnu.no, Updated: Jul 25, 2011.