Optimized Inverse Nonlinear Function-Based Wiener Model Predictive Control for Nonlinear Systems

Aliskan İ.

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, vol.46, no.10, pp.10217-10230, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 10
  • Publication Date: 2021
  • Doi Number: 10.1007/s13369-021-05681-w
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Communication Abstracts, Metadex, Pollution Abstracts, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.10217-10230
  • Keywords: Process control, State-space model predictive control, System identification, Wiener model, Offset-free control, ADAPTIVE-CONTROL, PH CONTROL, IDENTIFICATION, ALGORITHM, DESIGN, NOISE, MPC
  • Yıldız Technical University Affiliated: No


In model predictive control applications, the static nonlinear function of the Wiener model is an obstacle to forming a quadratic cost function. That case results in suboptimal control actions. As is known, a Wiener model predictive controller makes predictions employing the linear block of the Wiener model, and so its control solution is optimal due to the quadratic cost function. If the actual process output is used as a parameter in the empirical model, the inverse of the nonlinear function cannot be obtained as the roots of the nonlinear function in an offline manner, and the Wiener model predictive controller cannot be developed. Having regard to those situations, this paper introduces an offset-free Wiener model-based predictive controller with optimized inverse nonlinear function for the perturbed neutralization processes. Here, to facilitate accounts in the prediction horizon, an autoregressive with an external input model is preferred as the linear element of the Wiener model, and the least-squares-based optimization method is proposed to get the inverse of the nonlinear function. In this way, the model correction term can be employed to achieve an offset-free controller just as employed in the linear model predictive controller. Finally, the control performance of the developed controller is confirmed via comparative MATLAB/Simulink studies.