Robust Multivariable Control System Designs Through Real-Time Supervisory Knowledge-Based Systems

Kendra, Scott J.; Basila, Michael R.; Çınar, Ali

doi:10.1016/b978-0-08-042229-9.50062-8

Cited by 2 publications

(3 citation statements)

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“…6 A multivariable chi-squared test on the output and prediction errors has been suggested to help diagnosis MPM in Kesavan and Lee. 16 In Kendra and Cinar 17 and Kendra et al 18 the multivariable complementary sensitivity functions are identified in the frequency domain, and are compared to their design specifications. Any significant differences would imply the presence of MPM.…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of Poor Control Loop Performance Due to Model−Plant Mismatch

Selvanathan

Tangirala

2010

Ind. Eng. Chem. Res.

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Model-plant mismatch is a crucial factor that influences the performance of a closed-loop system. A new approach is proposed for the diagnosis of poor control loop performance due to model-plant mismatch (MPM) in the internal model control framework. In particular, the objective is to identify the mismatch in specific components of a transfer function model, namely, the gain, time-constant, and delay from closed-loop operating data. A new quantity G p /G m , called the plant model ratio (PMR) in the frequency domain is introduced as a measure of model-plant mismatch. It is shown that there exists a unique signature in PMR for each combination of mismatch in model parameters, which is the key step in the proposed method. A method to estimate the PMR from closed-loop operating data is provided. Theoretical and practical aspects of the mapping between the type of MPM and the proposed PMR are presented. Simulation studies demonstrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Diagnosis of Poor Control Loop Performance Due to Model−Plant Mismatch

Selvanathan

Tangirala

2010

Ind. Eng. Chem. Res.

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“…Controller input-output selections are determined during the model reduction process from the lists of available plant inputs and outputs. Positive results have been obtained for the case when the process has changed operating conditions, requiring the expert system to perform automatic controller restructuring (Kendra, 1992).…”

Section: Automatic Controller Restructuringmentioning

confidence: 89%

“…Rejection of disturbances, such as total flow variations, and inlet coolant temperature variations were also considered and reported elsewhere (Kendra, 1992).…”

Section: Experimental Test Suitementioning

confidence: 99%

Intelligent process control with integrated robust multivariable controllers and real-time supervisory knowledge-based systems

Kendra

Basila

Çınar

Proceedings of 8th IEEE International Symposium on Intelligent Control

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The application of a supervisory knowledge-based system (KBS) to provide robust multivariable control of a chemical reaction process with reduced conservatism is examined. The supervisory KBS is capable of monitoring the process t o detect process and control system faults or deteriorating control system performance due to changes in the process behavior or operating conditions. If a fault or change in performance is detected, the KBS formulates and implements the necessary corrective action. This paper focuses on control loop tuning. The underlying mechanisms are discussed and validation results, using a rigorous simulation of the chemical reactor process, are presented.

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Robust Multivariable Control System Designs Through Real-Time Supervisory Knowledge-Based Systems

Cited by 2 publications

References 1 publication

Diagnosis of Poor Control Loop Performance Due to Model−Plant Mismatch

Diagnosis of Poor Control Loop Performance Due to Model−Plant Mismatch

Intelligent process control with integrated robust multivariable controllers and real-time supervisory knowledge-based systems

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