Development of Modern Control Strategies for Multivariable Processes Using a Multiobjective Optimization Approach and Bioinspired Algorithms (DEC-MOBA)

The DEC-MOBA project addresses the inherent complexity of controlling multivariable processes, where interactions between variables hinder the application of classical strategies. Given the ambiguities of traditional methods like the Relative Gain Array (RGA), this research proposes the use of multi-objective evolutionary algorithms (MOEA) and bio-inspired techniques to optimize the selection and tuning of control loops. The methodology includes a literature review, mathematical modeling of processes, and experimental implementation on laboratory prototypes at the Universidad Politécnica Salesiana (UPS). Multidimensional visualization tools are used to analyze data and ensure more precise technical decision-making. The expected impact focuses on technological innovation, providing a robust solution for non-linear and highly complex systems. Furthermore, the project strengthens the institution's research capacity, promoting student training and the generation of indexed scientific products, while contributing to energy efficiency through more precise process control.<br/><br/><b>Goal</b>: <br/>Develop modern control strategies for multivariable processes using a multi-objective optimization approach and bio-inspired algorithms. The project aims to overcome the limitations of traditional methodologies in complex systems.<br/><br/><b>Research lines</b>: <br/>Control engineering and automation technologies<br/>Artificial intelligence and machine learning