Optimal sensor network design for multirate systems

Journal of Process Control

Volumn 18, Issue 6, 2008, Pages 594-609

  Kadu, Sachin C ^a,b   Bhushan, Mani ^a   Gudi, Ravindra ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

^b BHABHA ATOMIC RESEARCH CENTRE   (India)

Author keywords

Multiobjective optimization; Multirate Kalman filter; Sensor network design

Indexed keywords

COST ACCOUNTING; KALMAN FILTERS; MEASUREMENT THEORY; MULTIOBJECTIVE OPTIMIZATION; OPTIMAL CONTROL SYSTEMS; PROCESS CONTROL;

MULTIRATE KALMAN FILTERS; MULTIRATE SYSTEMS; QUADRUPLE TANK PROCESSES; SAMPLING FREQUENCIES; TOTAL MEASUREMENT COSTS;

SENSOR NETWORKS;

EID: 43149084818     PISSN: 09591524     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jprocont.2007.10.002     Document Type: Article

References (27)

1. Johansson K.H. The quadruple-tank process: a multivariable laboratory process with an adjustable zero. IEEE Trans. Control Syst. Tech. 8 3 (2000) 456-465
2. Downs J.J., and Vogel E.F. A plant-wide industrial process control problem. Comput. Chem. Eng. 17 3 (1993) 245-255
3. Mellefont D.J., and Sargent R.W.H. Selection of measurements for optimal feedback control. Ind. Eng. Chem. Process Des. Dev. 17 4 (1978) 549-552
4. Mehra R.K. Optimization of measurement schedules and sensor designs for linear dynamic systems. IEEE Trans. Automat. Control 21 1 (1976) 55-64
5. Waldraff W., Dochain D., Bourrel S., and Magnus A. On the use of observability measures for sensor location in tubular reactor. J. Process Control 8 5 (1998) 497-505
6. Muske K.R., and Georgakis C. Optimal measurement system design for chemical processes. AIChE J. 49 6 (2003) 1488-1494
7. Musulin E., Benqlilou C., Bagajewicz M.J., and Puigjaner L. Instrumentation design based on optimal Kalman filtering. J. Process Control 15 (2005) 629-638
8. Ali Y., and Narasimhan S. Sensor network design for maximizing reliability of linear processes. AIChE J. 39 (1993) 820-828
9. Bagajewicz M., and Cabrera E. New MILP formulation for instrumentation network design and upgrade. AIChE J. 48 10 (2002) 2271-2282
10. Bhushan M., and Rengaswamy R. Comprehensive design of a sensor network for chemical plants based on various diagnosability and reliability criteria.1. Framework. Ind. Eng. Chem. Res. 41 (2002) 1826-1839
11. Bagajewicz M., and Cabrera E. Pareto optimal solutions visualization techniques for multiobjective design and upgrade of instrumentation networks. Ind. Eng. Chem. Res. 42 (2003) 5195-5203
12. Bagajewicz M. A review of techniques for instrumentation design and upgrade in process plants. The Canadian J. Chem. Eng. 80 (2002) 3-16
13. Kranc G.M. Input-output analysis of multirate feed-back systems. IEEE Trans. Automat. Control 3 (1957) 21-28
14. Gudi R.D., Shah S.L., and Gray M.R. Adaptive multirate state and parameter estimation strategies with application to a bioreactor. AIChE J. 41 11 (1995) 2451-2464
15. Li D., Shah S.L., and Chen T. Identification of fast-rate models from multirate data. Int. J. Control 74 7 (2001) 680-689
16. Wang J., Chen T., and Huang B. Multirate sampled-data systems: computing fast-rate models. J. Process Control 14 (2004) 79-88
17. Deb K., Pratap A., Agarwal S., and Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6 2 (2002) 182-197
18. Maybeck P.S. Stochastic Models, Estimation, and Control vol. 1 (1979), Academic Press, New York pp. 1-16
19. Gelb A. Applied Optimal Estimation (1988), The MIT Press, England pp. 102-155
20. Lennartson B. Periodic solutions of Riccati equations applied to multirate sampling. Int. J. Control 48 3 (1988) 1025-1042
21. Nagrath I.J., and Gopal M. Control System Engineering. second ed. (1982), Wiley Estern limited, India pp. 493-503
22. Wonham W.M. On a matrix Riccati equation of stochastic control. SIAM J. Control 6 4 (1968) 681-697
23. Nicolao G.D. Cyclomonotonicity and stabilizability properties of solutions of the difference periodic Riccati equation. IEEE Trans. Automat. Control 37 9 (1992) 1405-1410
24. Deb K. Multi-Objective Optimization using Evolutionary Algorithms (2003), John Wiley & Sons (Asia) Pte. Ltd., Singapore
25. Bhaskar V., Gupta S.K., and Ray A.K. Applications of multiobjective optimization in chemical engineering. Reviews Chem. Eng. 16 (2000) 1-54
26. Fleming P.J., and Purshouse R.C. Evolutionary algorithms in control systems engineering: a survey. Control Engg. Practice 10 (2002) 1223-1241
27. Ricker N.L., and Lee J.H. Nonlinear modeling and state estimation for the Tennessee Eastman challenge process. Comput. Chem. Eng. 19 9 (1995) 983-1005