DOI:
https://doi.org/10.14483/23448350.22613Published:
08/30/2024Issue:
Vol. 50 No. 2 (2024): May-August 2024Section:
Research ArticlesComparative Analysis of Control Algorithms for Mobile Robots under Noise Conditions
Análisis comparativo de algoritmos de control para robots móviles bajo condiciones de ruido
Keywords:
autonomous robots, control theory, mobile robots (en).Keywords:
robótica móvil, robots autónomos, teoría de control (es).Downloads
Abstract (en)
The control of mobile robots in dynamic environments poses unique challenges compared to traditional manipulators, particularly due to the influence of wheel configuration on robot dynamics. Despite extensive studies on pathfollowing algorithms, the inclusion of noise in simulations is often overlooked, which is critical for real-world applications. To address this gap, this paper provides a comprehensive evaluation of four popular control algorithms under various noise conditions, i.e., proportional-integral control, feedback linearization, Lyapunov-based control (LBC), and model predictive control (MPC). The algorithms were tested using a circular trajectory to ensure consistent and challenging conditions, and their performances were measured using the integral absolute error and mean squared error metrics. The results show that LBC and MPC offer superior robustness to noise, making them suitable for practical applications. This study contributes to the existing literature by highlighting the importance of considering noise in control algorithm evaluations and provides recommendations regarding the selection of appropriate controllers for mobile robots in noisy environments.
Abstract (es)
El control de robots móviles en entornos dinámicos presenta desafíos únicos en comparación con los manipuladores tradicionales, particularmente debido a la influencia de la configuración de las ruedas en la dinámica del robot. A pesar de los numerosos estudios sobre algoritmos de seguimiento de trayectorias, a menudo se pasa por alto la inclusión de ruido en las simulaciones, lo cual es crítico para las aplicaciones del mundo real. Para abordar esta brecha, este documento proporciona una evaluación exhaustiva de cuatro algoritmos de control populares bajo diversas condiciones de ruido, i.e., control proporcional-integral, linealización por retroalimentación, control basado en Lyapunov (LBC) y control predictivo basado en el modelo (MPC). Los algoritmos fueron probados utilizando una trayectoria circular para asegurar condiciones consistentes y desafiantes, y sus desempeños fueron medidos utilizando las métricas de error absoluto integral (IAE) y error cuadrático medio (MSE). Los resultados muestran que el LBC y el MPC ofrecen una robustez superior al ruido, lo que los hace adecuados para aplicaciones prácticas. Este estudio contribuye a la literatura existente al destacar la importancia de considerar el ruido en la evaluación de algoritmos de control, y proporciona recomendaciones respecto a la selección de controladores apropiados para robots móviles en entornos ruidosos.
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