Upgrade analysis of Digital Signal Processors (DSP) in control processes with a metrological approach
DOI:
https://doi.org/10.26507/paper.4441Keywords:
metrology, mechatronics, DSP, control, pressureAbstract
From the Metrology research group of the Universidad Tecnológica de Pereira, we analyze, in the line of education and metrological culture, how the technique is affected by the imminent technological update of specialized processors designed to manipulate digital signals in real time and how it is crucial to promote these changes to transfer the results of research to the productive sector.
The desire to boost the competitiveness of the national industry through the adoption of cutting-edge technologies is reflected in the data provided and specifically in the Digital Signal Processors concerning Analog-to-Digital Converters (ADC) and Digital-to-Analog Converters (DAC).
The incorporation of Artificial Intelligence (AI) in Digital Signal Processors (DSP) is a process that can vary in complexity from basic implementations to highly sophisticated systems.
The incorporation of Artificial Intelligence (AI) in Digital Signal Processors (DSP) is a process that can vary in complexity, from basic implementations to highly sophisticated systems.
Undoubtedly, the evolution and upgrading of DSPs can be contained in:
- Increase in Processing Power.
- Integration with Artificial Intelligence
- FPGA Technology
- Improvements in Energy Efficiency
- High Level Programming Languages
The integration of up-to-date DSPs in mechatronics engineering opens up a range of possibilities, driving the creation of more intelligent, efficient and adaptive systems. In industrial automation and predictive maintenance, DSPs can analyze sensor data to detect anomalies and predict failures. In optimized process control, AI-enabled DSPs can optimize production processes in real time, adjusting parameters such as temperature, pressure and speed to maximize efficiency and quality.
To understand the DSP data, one must consider:
- ADC and DAC reliability of the DSP. This will determine the accuracy of the measurements and the resolution capability of the system.
- Dynamic Range: Defines the range of signals it can accurately process.
- Frequency Response
Calibration of a DSP is a complex process that requires a thorough understanding of the DSP specifications and the use of highly accurate reference equipment. Documentation and traceability are critical to ensure the quality and reliability of the measurements.
In this study, the implementation of an innovative pressure control system using machine vision and a DSP was developed, demonstrating how the combination of these technologies can provide reliable and adaptable pressure control for different processes.
Author Biographies
María Leyes Sánchez, Universidad Tecnológica de Pereira
Risaralda, Pereira
Docente transitorio, Facultad de ciencias básicas y programa de Ingeniería Mecatrónica
Henry William Peñuela Meneses, Universidad Tecnológica de Pereira
Electrical Engineer, Master in Physical Instrumentation. Professor, Faculty of Technology. Member of the Research Group METROLOGY, Research Semillero in Metrology. Technological University of Pereira
Marcela Botero Arbeláez, Technological University of Pereira
Electrical Engineer, Master in Physical Instrumentation. Professor, Faculty of Basic Sciences. Member of the Research Group METROLOGY, Research Semillero in Metrology. Technological University of Pereira
References
Emergent Vision Technologies. (s.f.). La evolución de la visión artificial. Recuperado de https://emergentvisiontec.com/es/tech-portal/evolution-of-machine-vision/
I-MAS. (2025, enero 29). La evolución de la visión artificial: del 2005 a la actualidad. Recuperado de https://i-mas.com/la-evolucion-de-la-vision-artificial-del-2005-a-la-actualidad
Linares, J. E., & Galvis, L. J. (2017). Control de presión usando visión artificial a través de un procesador digital de señales DSP [Trabajo de grado, Universidad Tecnológica de Pereira]. Repositorio UTP. https://repositorio.utp.edu.co/bitstreams/09048561-9926-464b-a139-7cccd3d062e0/download
Escobar, L. (2018). Procesamiento digital de señales [Apuntes]. Universidad Nacional Autónoma de México. http://odin.fi-b.unam.mx/labdsp/files/ADSP/2018-2/apuntes/dsp_apli0_17.pdf
Charre-Ibarra, Saida M, Alcalá-Rodríguez, Janeth A, López-Luiz, Norberto, & Durán-Fonseca, Miguel A. (2014). Sistema Didáctico de control de presión. Formación universitaria, 7(5), 33-40. https://doi.org/10.4067/S0718-50062014000500005
Festo, Estación de trabajo compacta MPS® PA con tramos de regulación de nivel, caudal, presión y temperatura, http://www.festo-didactic.com/int-es/learning-systems/automatizacion-de-procesos/ Acceso: 23 de marzo (2023).
Smith, J. A., & Brown, L. M. (2020). Digital signal processors in industrial control systems. IEEE Transactions on Industrial Electronics, 67(4), 1245. https://doi.org/10.1109/TIE.2020.2961234
García, P., & Martínez, R. (2018). Aplicación de visión artificial para el control de presión en procesos industriales. Revista Iberoamericana de Automática e Informática Industrial, 15(3), 45-53. https://doi.org/10.1016/j.riai.2018.05.002
Texas Instruments. (2021). TMS320C6000 DSP platform technical reference manual (Rev. 3). Texas Instruments. https://www.ti.com/lit/ug/spru187a/spru187a.pdf
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