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Complete model-free siding mode control (CMFSMC).

Quanmin Zhu1

  • 1Department of Engineering Design and Mathematics, University of the West of England, Frenchy Campus, Coldharbour Lane, Bristol, BS16 1QY, UK. quan.zhu@uwe.ac.uk.

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This study introduces a complete model-free sliding mode control (CMFSMC) framework for unknown nonlinear systems. It enables robust control without plant models, offering a transparent design for complex applications.

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Area of Science:

  • Control Engineering
  • Nonlinear System Dynamics
  • Robotics

Background:

  • Model-free control is crucial for systems with unknown dynamics.
  • Sliding mode control (SMC) offers robustness but often requires system models.
  • Existing model-free approaches have limitations in handling complex nonlinearities.

Purpose of the Study:

  • To develop a complete model-free sliding mode control (CMFSMC) framework.
  • To enable control of continuous-time non-affine nonlinear dynamic systems with unknown models.
  • To bridge the gap between model-based and model-free SMC designs.

Main Methods:

  • Introduction of two novel equalities to assign sliding function derivatives.
  • Design of a double SMC (DSMC) without plant models.
  • Development of a model-free state observer and a desired reference state vector design.
  • Incorporation of U-model based control (U-control) for system configuration.

Main Results:

  • The CMFSMC framework successfully controls nonlinear systems without requiring plant nominal models.
  • The U-control configuration effectively uses DSMC as a dynamic inverter and a model-free observer.
  • Simulated case studies validate the framework's performance and transparent design procedure.

Conclusions:

  • The presented CMFSMC framework provides a robust and model-independent solution for controlling complex nonlinear systems.
  • The design methodology is transparent, facilitating practical applications and future expansions.
  • The study demonstrates the feasibility of achieving precise system performance without prior knowledge of system dynamics.