|
Professor Morten
Hovd
Dr. Ing. Process Control, B.Sc. (Hons) Natural Gas
Engineering
Department of Engineering Cybernetics, |
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
Application:
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.
Explicit MPC:
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.