Bifurcation Analysis and Nonlinear Model Predictive Control of the Tri-Trophic Food Chain Model

 

This study delves into the intricate dynamics of a tri-trophic food chain model through bifurcation analysis and nonlinear model predictive control (NMPC). Using advanced computational tools like MATLAB’s MATCONT and Python's PYOMO, the research examines how bifurcation points (branch, limit, and Hopf) influence optimization and control strategies.

Key Highlights:

• Bifurcation Analysis: Identified key bifurcation points that lead to multiple steady-state solutions, crucial for understanding system stability.
• Nonlinear Control: Demonstrated that the presence of singularities aids NMPC calculations, achieving an optimal "Utopia solution."
• Tools Used: MATLAB (MATCONT) for bifurcation analysis and Python (PYOMO) for multi-objective optimization.

This work reaffirms the nonlinear complexity of food chain models and showcases the potential of bifurcation insights in enhancing predictive control techniques.

For a deeper dive into the findings and methodologies, check out the full article.

Content Details:-

Corresponding author: Lakshmi N Sridhar*

Full Length Article: Bifurcation Analysis and Nonlinear Model Predictive Control of the Tri-Trophic Food Chain Model

Journal: Austin Chemical Engineering

Disclaimer: This content is not owned or created by us. It has been sourced from the respective site, Austin Publishing Group, and is intended purely for study and educational purposes. All rights belong to the original authors and publishers.