Robust controller design for a class of MIMO nonlinear systems using TOPSIS function-link fuzzy cerebellar model articulation controller and interval type-2 fuzzy compensator

Article Overview

This study develops an adaptive control framework for multiple-input, multiple-output (MIMO) nonlinear systems with limited prior knowledge of the plant model. The framework combines a function-link fuzzy cerebellar model articulation controller (FLFCMAC), a function-link network (FLN), and an interval type-2 fuzzy logic compensator. The TOPSIS method is used to identify influential fuzzy inference rules.

Online adaptation laws update the controller parameters to address disturbances and model uncertainty. Closed-loop stability is investigated through Lyapunov analysis, and the control framework is assessed in simulations involving a caterpillar robot and an inverted double pendulum.

Key Features

  • Adaptive fuzzy control: Combines an FLFCMAC and an FLN for control of nonlinear MIMO systems.
  • Rule selection and uncertainty compensation: Uses TOPSIS to prioritize fuzzy rules and an interval type-2 fuzzy compensator to address uncertainty.
  • Stability and simulation studies: Employs Lyapunov-based analysis and evaluates performance on two nonlinear systems.

Figures

Trajectory tracking of the controlled states.

Trajectory tracking of the controlled states.

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Citation

@article{sepahvand2023robust,
  title={Robust controller design for a class of MIMO nonlinear systems using TOPSIS function-link fuzzy cerebellar model articulation controller and interval type-2 fuzzy compensator},
  author={Sepahvand, Shayan and Amiri, Niloufar and Pourgholi, Mahdi and Fakhari, Vahid},
  journal={Iranian Journal of Fuzzy Systems},
  volume={20},
  number={5},
  pages={89--107},
  year={2023},
  publisher={University of Sistan and Baluchestan}
}