Este trabajo presenta una metodología híbrida que combina controladores PID y LQR para optimizar la estabilidad y precisión de un dron en vuelo. La técnica emplea el ajuste de ganancia (Kpp) mediante el método de colocación de polos, logrando una respuesta dinámica mejorada. Los resultados muestran una sincronización efectiva entre control de altitud y actitud, destacando la robustez del sistema frente a perturbaciones externas. Considera la introducción de un mecanismo de weight scheduling adaptativo que ajusta en tiempo real la combinación de las acciones PID y LQR partiendo de la evaluación de una función de Lyapunov extendida. El artículo se enfoca también en el diseño de un modelo matemático a partir de un sistema físico.

The growing accumulation of microplastics in marine environments demands fast and accurate analytical methods for polymer identification. This study presents a new canonical spectral transformation (CST) strategy designed to extract the most relevant information of Raman spectra and enhance the performance of artificial intelligence (AI) models in the classification of microplastics. Using the Marine Plastic Database (MPDB) as the source of Raman spectra, five supervised models—k-Nearest Neighbor (KNN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and a one-dimensional Convolutional Neural Network (CNN-1D)—were trained and evaluated under both typical (conventional methodology) and CST workflows using 500 noisy samples per category. The CST consists of representing a Raman spectra in a vector where only the magnitude peaks of the most relevant frequency bands of the spectra are retained and the remaining values are null. This CST minimizes the inclusion of non-target data reaching the AI models. All models achieved higher accuracy with CST, where CNN-1D achieved the most significant performance, increasing accuracy to 0.90. In addition, CNN-1D identified Polystyrene (PS) and Poly(methyl methacrylate) (PMMA) with a score of 100% and 99%, respectively. The results demonstrate that CST effectively enhances spectral feature extraction and can be generalized to other spectroscopic techniques, providing a scalable framework for AI-assisted microplastic identification in seawater samples.