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From data to diagnosis and control using generalized orthonormal basis filters. Part II: model predictive and fault tolerant control

DSpace at IIT Bombay

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Title From data to diagnosis and control using generalized orthonormal basis filters. Part II: model predictive and fault tolerant control
 
Creator PATWARDHAN, SC
MANUJA, SEEMA
NARASIMHAN, SHANKAR
SHAH, SIRISH L
 
Subject computer simulation
predictive control system
sensors
kalman filtering
 
Description Given a state space model together with the state noise and measurement noise characteristics, there are well established procedures to design a Kalman filter based model predictive control (MPC) and fault diagnosis scheme. In practice, however, such disturbance models relating the true root cause of the unmeasured disturbances with the states/outputs are difficult to develop. To alleviate this difficulty, we reformulate the MPC scheme proposed by K.R. Muske and J.B. Rawlings [Model predictive control with linear models, AIChE J. 39 (1993) 262–287] and the fault tolerant control scheme (FTCS) proposed by J. Prakash, S.C. Patwardhan, and S. Narasimhan [A supervisory approach to fault tolerant control of linear multivariable systems, Ind. Eng. Chem. Res. 41 (2002) 2270–2281] starting from the innovations form of state space model identified using generalized orthonormal basis function (GOBF) parameterization. The efficacy of the proposed MPC scheme and the on-line FTCS is demonstrated by conducting simulation studies on the benchmark shell control problem (SCP) and experimental studies on a laboratory scale continuous stirred tank heater (CSTH) system. The analysis of the simulation and experimental results reveals that the MPC scheme formulated using the identified observers produces superior regulatory performance when compared to the regulatory performance of conventional MPC controller even in the presence of significant plant model mismatch. The FTCS reformulated using the innovations form of state space model is able to isolate sensor as well as actuator faults occurring sequentially in time. In particular, the proposed FTCS is able to eliminate offset between the true value of the measured variable and the setpoint in the presence of sensor biases. Thus, the simulation and experimental study clearly demonstrate the advantages of formulating MPC and generalized likelihood ratio (GLR) based fault diagnosis schemes using the innovations form of state space model identified from input output data.
 
Publisher Elsevier
 
Date 2009-03-13T07:03:06Z
2011-11-25T18:59:43Z
2011-12-26T13:07:07Z
2011-12-27T05:55:09Z
2009-03-13T07:03:06Z
2011-11-25T18:59:43Z
2011-12-26T13:07:07Z
2011-12-27T05:55:09Z
2006
 
Type Article
 
Identifier Journal of Process Control 16(2), 157-175
0959-1524
http://dx.doi.org/10.1016/j.jprocont.2005.04.011
http://hdl.handle.net/10054/922
http://dspace.library.iitb.ac.in/xmlui/handle/10054/922
 
Language en