A Modified Approximate Internal Model-based Neural Control for the Typical Industrial Processes

Igic, Jasmin; Bozic, Milorad

doi:10.7251/els1418046i

Cited by 4 publications

(2 citation statements)

References 20 publications

(29 reference statements)

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“…A coupled tanks system, which consists of a joint of two tanks together through pipes in order to reserve water at the operating height level, is an important study in the control engineering and process industries [1]. The applications of DOI: 10.4236/apm.2019.…”

Section: Introductionmentioning

confidence: 99%

“…94014 318 Advances in Pure Mathematics the coupled tanks system have been widely used in the real-world process, for example, petrochemical, waste-water treatment, and purification [2] [3]. Essentially, the coupled tanks modeling provides a configurable process control experiment to engineers and researchers such that a wide array of modeling and control-related laboratory works on liquid control can be performed in advance [1].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Expanded Optimal Control Policy for a Coupled Tanks System with Random Disturbance

Kek¹,

Sim²,

Chen³

2019

APM

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In this paper, an expanded optimal control policy is proposed to study the coupled tanks system, where the random disturbance is added into the system. Since the dynamics of the coupled tanks system can be formulated as a nonlinear system, determination of the optimal water level in the tanks is useful for the operation decision. On this point of view, the coupled tanks system dynamics is usually linearized to give the steady state operating height. In our approach, a model-based optimal control problem, which is adding with adjusted parameters, is considered to obtain the true operating height of the real coupled tanks system. During the computation procedure, the differences between the real plant and the model used are measured repeatedly, where the optimal solution of the model used is updated. On this basis, system optimization and parameter estimation are integrated. At the end of the iteration, the iterative solution approximates to the correct optimal solution of the original optimal control problem, in spite of model-reality differences. In conclusion, the efficiency of the approach proposed is shown.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%