Manfred Morari

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Manfred Morari
Born1951 (age 7071)
Awards Donald P. Eckman Award (1980)
NSF Presidential Young Investigator (1984)
AIChE Colburn Award (1984)
Curtis W. McGraw Research Award (1989)
U.S. National Academy of Engineering (1993)
IEEE Control Systems Award (2005)
Richard E. Bellman Control Heritage Award (2011)
Fellow of IEEE
Scientific career
Fields Control theory
Institutions University of Wisconsin, Madison,
California Institute of Technology,
ETH Zurich,
University of Pennsylvania

Manfred Morari (born 1951) is a world-leading control theorist who has made pioneering contributions to the theory and applications of Model Predictive Control, Internal Model Control (IMC) and Hybrid Systems. His book on Robust Process Control is considered to be definitive text on the subject. He is currently Peter and Susanne Armstrong Faculty Fellow at the University of Pennsylvania. He received his Ph.D. in Chemical Engineering from the University of Minnesota in 1977. Dr. Morari held positions at the University of Wisconsin, Madison from 1977–1983, the California Institute of Technology from 1983-1991, and the Swiss Federal Institute of Technology in Zurich ETH Zurich. He is considered as pioneer in field of Model Predictive Control, [1] Control of Hybrid Systems, [2] Internal Model Control (IMC), [3] and robust control. [4] [5]

In recognition of his research contributions he received numerous awards, among them the Donald P. Eckman Award and the John R. Ragazzini Award of the Automatic Control Council, the Allan P. Colburn Award and the Professional Progress Award of the AIChE, the Curtis W. McGraw Research Award of the ASEE, Doctor Honoris Causa from Babes-Bolyai University, Fellow of IEEE and IFAC, and the IEEE Control Systems Field Award. He was also elected a member of the US National Academy of Engineering in 1993 for analysis of the effects of design on process operability and the development of techniques for robust process control. Manfred Morari has held appointments with Exxon and ICI plc and serves on the technical advisory boards of several major corporations. He received in 2005 the IEEE Control Systems Award, [6] [7] and in 2011 the Richard E. Bellman Control Heritage Award. [8] [9]

Related Research Articles

Model predictive control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. It has been in use in the process industries in chemical plants and oil refineries since the 1980s. In recent years it has also been used in power system balancing models and in power electronics. Model predictive controllers rely on dynamic models of the process, most often linear empirical models obtained by system identification. The main advantage of MPC is the fact that it allows the current timeslot to be optimized, while keeping future timeslots in account. This is achieved by optimizing a finite time-horizon, but only implementing the current timeslot and then optimizing again, repeatedly, thus differing from a linear–quadratic regulator (LQR). Also MPC has the ability to anticipate future events and can take control actions accordingly. PID controllers do not have this predictive ability. MPC is nearly universally implemented as a digital control, although there is research into achieving faster response times with specially designed analog circuitry.

In control theory, robust control is an approach to controller design that explicitly deals with uncertainty. Robust control methods are designed to function properly provided that uncertain parameters or disturbances are found within some set. Robust methods aim to achieve robust performance and/or stability in the presence of bounded modelling errors.

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References

  1. Garcia, C.E.; Pret, D.M.; Morari, M (1989), "Model Predictive Control - Theory and Practice - A Survey", Automatica, 25 (3): 335–348, doi:10.1016/0005-1098(89)90002-2
  2. Bemporad, A; Morari, M (1999), "Control of systems integrating logic, dynamics, and constraints", Automatica, 35 (3): 407–427, doi:10.1016/s0005-1098(98)00178-2
  3. Garcia, C.E.; Morari, M (1982), "Internal Model Control 1 A Unifying Review and Some New Results", Industrial & Engineering Chemistry Process Design and Development, 21 (2): 308–323, doi:10.1021/i200017a016
  4. Kothare, M.V.; Balakrishnan, V.; Morari, M (1996), "Robust constrained model predictive control using linear matrix inequalities", Automatica, 32 (10): 1361–1379, doi:10.1016/0005-1098(96)00063-5
  5. Morari, M; Zafiriou, E (1989), Robust Process Control, Prentice Hall, ISBN   978-0-13-782153-2
  6. "IEEE Control Systems Award Recipients" (PDF). IEEE . Retrieved March 30, 2011.
  7. "IEEE Control Systems Award". IEEE Control Systems Society. Archived from the original on 2010-12-29. Retrieved March 30, 2011.
  8. "Prof. Morari received Bellman control award". ETH . Retrieved August 16, 2012.
  9. "Richard E. Bellman Control Heritage Award". American Automatic Control Council . Retrieved February 10, 2013.