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SUMMARY:Self-optimizing control.
DTSTART:20101119T101500
DTSTAMP:20260410T224010Z
UID:75225c7ad156e4e4fedfeddfeb2d1ded3f5b34fcbdacebd518909dc8
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. S. Skogestad\, Department of  Chemical Engineering\,  NT
 NU Trondheim\,  Norway.\nSelf-optimizing control deals with the selection 
 of measurements (y) or measurement combinations as controlled variables (C
 Vs)\, c = Hy.  The issue is to select H. This is an important decision whi
 ch is usually not view as a decision at all\, and certainly not treated sy
 stematically. The term "self-optimizing" refers to cases where one can kee
 p constant setpoints for the CVs\, without any need to reoptimize when dis
 turbances (d) occur.\n\nIn the talk\, some approaches for selecting self-o
 ptimizing variables are reviewed\, including the very simple nullspace met
 hod which is to select H such that HF=0 where F = dyopt/dd is the optimal 
 sensitivity. More generally\, one should select H to minimize the norm of 
 HF. Normally\, F is obtained from the model\, but it can also be used to f
 ind patterns in optimal data. It is interesting that the focus is on the s
 mall singular values of the data matrix F\,and not on the large singular v
 alues as is normally the case with "chemiometric" methods.\n\nIt is argued
  that self-optimizing control is not an alternative to real-time optimizat
 ion (RTO)\, NCO tracking or model predictive control (MPC)\, but is to be 
 seen as complementary. In self-optimizing control we determine controlled 
 variables (CV). Preferably\, the CV set points are kept constant\, but the
 y may also be adjusted using RTO or NCO tracking. In any case\, a good cho
 ice of CVs will reduce the frequency of setpoint changes by RTO or NCO tra
 cking. \n\nWhen selecting self-optimizing CVs\, a set of disturbances has 
 to be assumed\, as unexpected disturbances are not rejected in SOC. On the
  other hand\, RTO and NCO tracking adapt the inputs at given sample times 
 without any assumptions on what disturbances occur. By using NCO tracking 
 in the optimization layer and SOC in the control layer below\, we demonstr
 ate that the methods are complementary. This combination allows for fast o
 ptimal action for the expected disturbances (by SOC)\, while other disturb
 ances are compensated by\nNCO tracking on a slower time scale.
LOCATION:ME C2405
STATUS:CONFIRMED
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