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Model Based Predictive Control of Multivariable Hammerstein Processes with Fuzzy Logic Hypercube Interpolated Models.

Daniel Cavalcanti Jeronymo1,2, Antonio Augusto Rodrigues Coelho2

  • 1Computer Engineering Department, Federal University of Technology, Toledo, Paraná, Brazil.

Plos One
|September 23, 2016
PubMed
Summary
This summary is machine-generated.

The Fuzzy Logic Hypercube Interpolator (FLHI) models nonlinear systems and their inverses. This novel fuzzy logic approach offers versatile interpolation for advanced process control applications.

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Area of Science:

  • Control Engineering
  • Fuzzy Logic Systems
  • Nonlinear System Modeling

Background:

  • Hammerstein nonlinearities present challenges in modeling and control.
  • Existing interpolation methods may lack flexibility for complex nonlinear systems.

Purpose of the Study:

  • Introduce the Fuzzy Logic Hypercube Interpolator (FLHI) for nonlinear system modeling.
  • Demonstrate FLHI's capability in handling multiple-input systems with Hammerstein nonlinearities.
  • Apply FLHI for modeling both functions and their inverses.

Main Methods:

  • Developed FLHI based on a Takagi-Sugeno fuzzy inference system.
  • Utilized membership functions as kernel functions for N-dimensional interpolation.
  • Configured FLHI to emulate various interpolation behaviors (nearest-neighbor, linear, cubic, spline, Lanczos).

Main Results:

  • Successfully modeled single-input, multiple-input single-output (MISO), and multiple-input multiple-output (MIMO) Hammerstein nonlinear systems.
  • Achieved good performance in set-point tracking, control variation, and robustness.
  • Demonstrated effective modeling of inverse Hammerstein nonlinearities.

Conclusions:

  • FLHI provides a robust solution for modeling static nonlinearities and their inverses.
  • FLHI is applicable for implementing output compensators in Model Based Predictive Control (MBPC), specifically Dynamic Matrix Control (DMC).
  • The method shows significant potential for enhancing control performance in complex industrial processes.