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Adaptive back-stepping cancer control using Legendre polynomials.

Saeed Khorashadizadeh1, Ali Akbarzadeh Kalat2

  • 1Faculty of Electrical and Computer Engineering, University of Birjand, 97175/615 Birjand, Iran. s.khorashadizadeh@birjand.ac.ir.

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This study introduces a novel model-free controller for cancer immunotherapy, simplifying treatment design and reducing computational load. The new method effectively reduces tumor cells using Legendre polynomials for uncertainty estimation.

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Area of Science:

  • Biomedical Engineering
  • Control Theory
  • Computational Biology

Background:

  • Cancer treatment control theory relies on mathematical models of immune cells and tumor dynamics.
  • Existing non-linear controllers for cancer immunotherapy often involve complex structures like Neuro-Fuzzy controllers.
  • Interleukin-2 (IL-2) concentration is a key factor in modulating immune responses against tumors.

Purpose of the Study:

  • To develop a novel model-free controller for Multi-Input Multi-Output (MIMO) cancer immunotherapy.
  • To simplify the design procedure and reduce computational load compared to existing methods.
  • To effectively estimate and compensate for system uncertainties in cancer treatment.

Main Methods:

  • Utilized a back-stepping design procedure for controller synthesis.
  • Employed Legendre polynomials for function approximation, uncertainty estimation, and compensation.
  • Designed a model-free controller for MIMO cancer immunotherapy, focusing on tumor cell reduction.

Main Results:

  • The proposed controller demonstrated efficient and fast reduction of tumor cells in simulations.
  • Legendre polynomials provided a simpler structure for uncertainty estimation compared to neural networks.
  • The closed-loop system effectively handled various system uncertainties.

Conclusions:

  • The novel controller simplifies MIMO cancer immunotherapy design and reduces computational demands.
  • Legendre polynomials offer an effective and computationally efficient alternative for uncertainty management in cancer treatment.
  • The proposed method shows significant potential for improving cancer treatment strategies.