New System for Angular Velocity Estimation for a First-Order Manipulator Using Artificial Intelligence and Sliding Mode Differentiator

https://doi.org/10.58571/CNCA.AMCA.2022.055

Resumen: On a NVIDIA Jetson Nano device, this study illustrates a unique and original usage of automated control and artificial intelligence algorithms for angular velocity estimates of a first-order manipulator device. A platform can be used as a position estimation platform using computer vision and three state estimation algorithms: sliding mode differentiator, high gain observer, and static filter. A hybrid system for process performance improvement is proposed using computer vision and three state estimation algorithms: sliding mode differentiator, high gain observer, and static filter. The results of numerical simulations are provided, as well as real-time judgments. The ITAE and IAE indices reveal that the sliding mode differentiator is much superior in angular velocity estimation for position signals utilizing artificial intelligence sensors.

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Tejeda-Ochoa, D., Peralta-Peñuñuri, G., Gonzalez-Huitrón, V., Baray-Arana, R., Rodriguez-Rangel, H. & Rodriguez-Mata, A. A New System for Angular Velocity Estimation for a First-Order Manipulator Using Artificial Intelligence and Sliding Mode Differentiator. Memorias del Congreso Nacional de Control Automático, pp. 362-367, 2022. https://doi.org/10.58571/CNCA.AMCA.2022.055

Palabras clave

Modelado e Identificación de Sistemas; Robótica y Mecatrónica; Redes Neuronales

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