System Modeling And Optimization Under Vector-valued Criteria

The integrated problem of optimization and parameter estimation of a given system is addressed in the context of vector-valued optimization techniques. The validity of the existing convergence properties of the basic two-step approach for solving the joint problem of scalar optimization and parameter estimation is extended for the vector-valued case under adequate conditions. Some elements of decision analysis are incorporated into the joint problem in order to reflect conflicting situations derived from the simultaneous consideration of multiple performance criteria. To illustrate the main aspects of the approach proposed, the problem of optimization and parameter estimation of a nonlinear dynamic batch reactor is considered. The results attest the computational feasiblity of the procedure and its usefulness as a qualitative tool for the synthesis of integrated control policies. © 1994.302331336Borges, Ferreira, Joint optimization and identification (1991) Internal Report n. 01/91, , UNICAMP, (in Portuguese)Brdys, Roberts, Optimal structures for steady-state adaptive optimizing control of large-scale industrial processes (1986) Int. J. Systems Sci., 17, pp. 1449-1474Chankong, Haimes, On the characterization of noninferior solutions of the vector optimization problem (1982) Automatica, 18, pp. 697-707Cohon, Church, Sheer, Generating multiobjective trade-offs: an algorithm for bicriteria problems (1979) Water Resources Research, 15, pp. 1001-1009Denbigh, Turner, (1984) Chemical Reactor Theory: An Introduction, , 3rd ed., Cambridge University Press, CambridgeEllis, Roberts, Measurement and modelling trade-offs for integrated system optimization and parameter estimation (1982) Large Scale Systems, 3, pp. 191-204Ferreira, Geromel, An interactive projection method for multicriteria optimization problems (1990) IEEE Trans. on Systems, Man and Cybernetics, 20 SMC, pp. 596-605Foss, Critique of chemical process control theory (1973) IEEE Transactions on Automatic Control, 18 AC, pp. 646-652Geoffrion, Solving Bicriterion Mathematical Programs (1967) Operations Research, 5, pp. 39-54Haimes, Wismer, A computational approach to the combined problem of optimization and parameter identification (1972) Automatica, 8, pp. 337-347Hwang, Masud, Multiobjective decision-making methods and applications (1979) Lecture Notes in Economics and Mathematical Systems, , Springer, BerlinKümmel, Seborg, A contemplative stance for chemical process control (1987) Automatica, 23, pp. 801-802Luenberger, (1984) Linear and Nonlinear Programming, , Addison Wesley, MALuyben, (1973) Process Modeling, Simulation and Control for Chemical Engineers, , McGraw-Hill Chemical Engineers Series, NYMcGrew, Haimes, Parametric solution to the joint system identification and optimization problem (1974) J. of Optimization Theory and Applications, 3, pp. 583-605Payne, Polak, Collins, Miesel, An algorithm for bicriteria optimization based on the sensitivity function (1975) IEEE Transactions on Automatic Control, 20 AC, pp. 546-548Rarig, Haimes, Risk/dispersion index method (1983) IEEE Trans. on Systems, Man and Cybernetics, 13 SMC, pp. 317-328Roberts, An algorithm for steady-state system optimization and parameter estimation (1979) Int. J. Systems Sci., 10, pp. 719-734Sorenson, Parameter estimation (1980) Control and Systems Theory, 9. , Marcel Dekker, NYTatjewski, Roberts, Newton-like algorithm for integrated system optimization and parameter estimation technique (1987) Int. J. Systems Sci., 46, pp. 1155-1170Vachtsevanos, Lp-convergence of a process identification algorithm (1979) International Journal of Systems Science, 6, pp. 401-407Yu, (1985) Multiple-Criteria Decision Making: Concepts, Techniques and Extensions, , Plenum, N