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This research line contributes to the analysis, modeling, and validation of dynamic systems, both linear and nonlinear, as well as the development of new controller design methods to meet stability and performance requirements. Polynomial models are developed for applications in biomedical systems, neural networks, fuzzy systems, and agent-based models (ABMs).

In the field of controller design, the focus includes Industry 4.0 paradigms, such as sampled-data control, networked control, and data-driven control. Methodologies for controller analysis and design are developed to ensure closed-loop robustness for linear and nonlinear, uncertain, and multivariable systems, whether time-invariant or time-varying.

Performance criteria include exponential stability, input-state stability, regional eigenvalue allocation, guaranteed H-2 or H-infinity costs. Various synthesis strategies are employed, including linear matrix inequalities (LMIs), multi-objective optimization, global and non-convex optimization approaches, and computational intelligence techniques such as evolutionary computing.

The techniques developed are driven by practical challenges, addressed through mathematical formalism to ensure the quality of the proposed solutions. Whenever possible, experiments and validation of the developed methods are conducted, bridging the gap between theory and practice.