Jeremiah, S.S. and Zabiri, H. and Ramasamy, M. and Teh, W.K. and Kamaruddin, B. and Mohd Amiruddin, A.A.A. (2019) Generic framework for valve stiction detection and compensation with ANFIS-activated dual-mode MPC. Journal of Process Control, 79. pp. 85-97. ISSN 09591524
Full text not available from this repository.Abstract
Due to its continuous motion, control valve performance tends to deteriorate over time due to the presence of static-friction or also known as stiction. This, in turn, leads to high variability in product quality and an increased frequency of valve maintenance. Model Predictive Control (MPC) based stiction compensation methods can remove oscillations caused by stiction but it assumes that stiction is known a �priori to exist in the related loops. To overcome this limitation, an integrated framework is proposed to automate the detection of stiction using only process variable and controller output while compensating for stiction with MPC. The detection algorithm, which was validated using industrial data, uses an adaptive neuro-fuzzy inference system (ANFIS). Out of the 78 benchmark industrial loops tested, the proposed Intuitive ANFIS-based Method (IAM) has a detection accuracy of 65, placing it on par with the best of the currently available methods reported in the literature for loops with stiction. Within the proposed framework, the detection component only activates the MPC-based compensation when needed. In a simulation of a multivariable process, it is demonstrated that the dual-mode MPC manages to eliminate oscillation caused by stiction with no chattering. This results in better overall performance when controlling a loop throughout the service life of the valve. © 2019 Elsevier Ltd
Item Type: | Article |
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Additional Information: | cited By 4 |
Uncontrolled Keywords: | Compensation (personnel); Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Inference engines; Model predictive control; Predictive control systems, Adaptive neuro-fuzzy inference system; ANFIS; Detection algorithm; Generic frameworks; Integrated frameworks; Multivariable process; On-line detection; Stiction compensations, Stiction |
Depositing User: | Mr Ahmad Suhairi UTP |
Date Deposited: | 10 Nov 2023 03:26 |
Last Modified: | 10 Nov 2023 03:26 |
URI: | https://khub.utp.edu.my/scholars/id/eprint/11511 |