Examinando por Materia "Controladores con aprendizaje por refuerzo"

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    Comparación de algoritmos de control tradicionales con control por medio de IA en un entorno simulado 2D
    (Universidad EAFIT, 2025) Penagos Ramírez, Mateo Fernando; Puerta Echandía, Alejandro
    In automation and control technology, the efficiency of control algorithms is crucial for the performance and safety of complex systems. Traditionally, Proportional–Integral–Derivative (PID) controllers have been the cornerstone of regulating these systems due to their simplicity and robustness, being used in more than 90% of industrial applications. Methods such as PID work very well in environments that are easy to model mathematically and have little variability; such systems can be found in industrial plants and production equipment. However, in dynamic and nonlinear environments, performance issues or difficulties in tuning their parameters may arise. This project aims to compare the performance of traditional control algorithms—specifically PID—with those based on neural networks trained using reinforcement learning techniques. To this end, a simulated 2D environment will be developed to replicate the dynamic behavior of a nonlinear system; in this case, a drone in flight was chosen. This nonlinear environment will allow evaluation of both types of controllers under a series of conditions and operational challenges, including stabilization, trajectory tracking, and response to external disturbances. The research will focus on measuring the accuracy, efficiency, and adaptability of each algorithm, providing an objective basis for comparison that considers key performance metrics. In addition, the inherent advantages and limitations of each approach will be analyzed, including implementation complexity, computational requirements, and scalability.