Augmented Model Based Double Iterative Loop Techniques for Integrated System Optimisation and Parameter Estimation of Large Scale Industrial Processes

Brdyś, M.; Abdullah, Nik Nairan; Roberts, P.D.

doi:10.1016/s1474-6670(17)55131-9

Cited by 2 publications

(2 citation statements)

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“…Then, there exist 0 < e l < 0.5, 0.5 < E, < 1 and e3 > 0, such that for every c, E (E,, e2) and cc E (0,c31, It is interesting to see that apart from the definitions of a and A, the sufficient conditions for the local convergence of the algorithm are actually the same as that of the single loop technique (BrdyS et al 1986 b). This means that the applicability conditions of this new 'double-loop algorithm are much weaker than those of the previous ones (BrdyS and .…”

Section: Theorem 3: Local Convergence Theoremmentioning

confidence: 99%

“…Optimality of the algorithm is examined, and sufficient conditions for local convergence are investigated. It is worth mentioning that the convergence conditions of this algorithm are much easier to satisfy than those of the previous double-loop techniques , BrdyS et al 1987 which are only provided fo; the linear quadratic case and are almost the same as that of the single-loop technique (BrdyS et al 1986 b). Computer simulation is used to demonstrate the behaviour of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

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New model-based double-loop iterative strategy for integrated system optimization and parameter estimation of large-scale industrial processes

Lin

Hendawy

Roberts

1988

International Journal of Control

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A novel hierarchical integrated system optimization and parameter estimation technique is described which determines the optimum steady state operation of an interconnected industrial process in spite of deficiencies in the model. The technique is iterative and involves successive solutions of system optimization and model parameter estimation problems, utilizing information feedback from the real process. Particular emphasis is given to a strategy where the coordination task is divided into two nested iterative loops, with the inner loop optimization containing a self-adaptive model. Derivatives of real process measurements required by the outer loop iteration are also used to update the model. New modifiers are introduced to cater for the output-dependent constraints. This model-based doubleloop iterative strategy retains an important practical advantage in that it reduces the required number of set point changes to the real process. Optimality properties and convergence conditions are investigated. A simulation study is also presented.

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Section: Theorem 3: Local Convergence Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%