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This study introduces a hybrid control framework using internal model concepts, sliding mode control (SMC), and fractional-order calculus for nonlinear systems with delays. The novel approach enhances system stability and robustness, demonstrated through simulations and a practical Arduino application.

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Area of Science:

  • Control Engineering
  • Nonlinear System Dynamics
  • Fractional-Order Calculus

Background:

  • Traditional control methods struggle with nonlinear systems exhibiting significant time delays.
  • Improving transient response and robustness in dead-time processes remains a challenge.
  • Integrating internal model concepts with sliding mode control offers potential for enhanced performance.

Purpose of the Study:

  • To propose a hybrid control framework combining internal model concepts, sliding mode control (SMC), and fractional-order calculus.
  • To develop a modified Smith predictor (SP) tailored for nonlinear systems with substantial delays.
  • To enhance the transient responses and robustness of controllers for dead-time processes.

Main Methods:

  • A modified Smith predictor (SP) is designed incorporating fractional-order concepts.
  • The predictive approach is integrated with a sliding mode control (SMC) controller.
  • A dynamical sliding mode controller is formulated by combining predictive and fractional-order elements.

Main Results:

  • Numerical simulations demonstrate the proposed controller's performance under step changes, external disturbances, and parametric uncertainty.
  • A real-world application on a TCLab Arduino kit shows good performance with minimal chattering.
  • The controller exhibited an aggressive response with increased overshoot during disturbance rejection, indicating a need for parameter tuning.

Conclusions:

  • The proposed hybrid fractional-order sliding mode controller effectively manages nonlinear systems with delays.
  • The controller demonstrates robustness against model mismatches and external disturbances.
  • Further optimization of tuning parameters is recommended to refine performance and controller action in specific scenarios.