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Adaptive Model Predictive Control for Wiener Nonlinear Systems

Abstract

Wiener model, which is one of the structures used in the modeling of nonlinear systems, consists of the cascade connection as that a dynamic linear system is followed in series by a static nonlinear function. Different approaches have been developed and proposed to control this kind of systems in the last decades. In this study, a model predictive control system with online identification support has been developed. The prominent feature of online system identification may be referred to as accommodating easily to severe changes in system parameters. The combination of MPC algorithm with online identification constitutes an adaptive model predictive control algorithm that can sense the input parameter variation. To assess the performance of the proposed control system, a strong acid–strong base chemical neutralization process without buffer solution is selected, and the controller is applied to the chemical process to verify its effectiveness in acidic, alkaline and neutral regions. Results obtained from MATLAB/Simulink studies confirm the performance of the controller that serves under variable system conditions.

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Correspondence to Ibrahim Aliskan.

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Aliskan, I. Adaptive Model Predictive Control for Wiener Nonlinear Systems. Iran J Sci Technol Trans Electr Eng 43, 361–377 (2019). https://doi.org/10.1007/s40998-018-0159-0

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  • DOI: https://doi.org/10.1007/s40998-018-0159-0

Keywords

  • Parameter estimation
  • Wiener system
  • Predictive control
  • Least squares