Multivariable adaptive neural network predictive control in the presence of measurement time-delay; application in control of Vinyl Acetate monomer process. (June 2018)

• Record Type: Journal Article
• Authors: Pazhooh, Faramarz; Shahraki, Farhad; Sadeghi, Jafar; Fakhroleslam, Mohammad
• Is Part Of: Journal of process control. Volume 66(2018)
• Page Start: 39
• Page End: 50
• Publication Date: 2018-06
• Subjects: Adaptive observers -- Predictive control -- Time-delay -- Neural networks -- Multi-input multi-output -- Vinyl acetate monomer process; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2018.03.003
• Languages: English
• ISSNs: 0959-1524

Abstract

Graphical abstract: Highlights: A MIMO controller is proposed for systems with delays and unmeasurable states. A MIMO neural network model-based state observer is designed. Stability of the proposed observer and controller is proved using Lyapunov theorem. The proposed controller is used as an integral part of plantwide controller of a VAM plant. Abstract: As an integral part of a plantwide control system for large-scale nonlinear systems with non-measurable states and time-delay in measured outputs, a multi-input multi-output (MIMO) adaptive neural network predictive controller (ANNPC) is presented. A neural network model-based observer is used in the structure of the proposed controller to estimate the unknown states. Then, an adaptive predictor is designed based on the observer and is employed to predict non-measurable states. Stability of the proposed observer and controller is proved using Lyapunov function theorem. The proposed controller is used as a part of the control system of a Vinyl Acetate monomer (VAM) process. A new partially-centralized structure is developed for plantwide control of the process and the efficiency of the proposed controller particularly in diminishing the effect of measurement time-delay is shown, in-silico, by numerical simulation of a VAM plant under control. The obtained results are compared with the results of the conventional PI-based control system of VAM process.