Nonlinear control designs and their application to cancer differentiation therapy

Area of Science:

• Biomedical Engineering
• Control Theory
• Computational Biology

Background:

• Cancer differentiation therapy aims to control cancer cell states.
• Existing control strategies have limitations in performance and robustness.

Purpose of the Study:

• To design and evaluate three novel controllers for cancer differentiation therapy.
• To compare the performance and robustness of new controllers against existing methods.

Main Methods:

• Development of a sigmoid-based controller incorporating an error-associated sigmoid term.
• Implementation of a polynomial dynamic inversion-based controller with a cubic error term for faster convergence.
• Design of a proportional-integral-derivative (PID) impulsive controller to enhance state convergence and reduce steady-state damping.

Main Results:

• All three designed controllers demonstrated superior performance in the considered cancer network model.
• The sigmoid-based controller reduced control effort during state transitions.
• The polynomial dynamic inversion-based controller achieved shorter convergence times.
• The PID impulsive controller exhibited significantly improved robustness compared to existing impulsive controllers, showing great potential for cancer differentiation therapy.

Conclusions:

• The novel sigmoid-based, polynomial dynamic inversion-based, and PID impulsive controllers offer enhanced performance for cancer differentiation therapy.
• The PID impulsive controller presents a promising advancement in robustness and efficacy for cancer treatment.
• These advanced control strategies hold significant potential for improving cancer differentiation therapy outcomes.