On-line method for optimal tuning of PID controllers using standard OPC interface
Introducción— El controlado PID es el algoritmo matemático mayormente utilizado como estrategia de control regulatorio en entornos industriales. Las aplicaciones son variadas; sin embargo, su respuesta depende del cálculo adecuado de sus tres parámetros: el proporcional, el derivativo y el integral....
- Autores:
-
Riano , Cristhian
Díaz-Rodríguez, Jorge Luis
Mejía Bugallo, Diego Armando
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9981
- Acceso en línea:
- https://hdl.handle.net/11323/9981
https://repositorio.cuc.edu.co/
- Palabra clave:
- Algoritmos genéticos
Sintonización automática
Optimización
Controlador PID
Genetic algorithms
Automatic tuning
Optimization
PID controller
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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On-line method for optimal tuning of PID controllers using standard OPC interface |
| dc.title.translated.none.fl_str_mv |
Método on-line para sintonización óptima de controladores PID utilizando interface estándar OPC |
| title |
On-line method for optimal tuning of PID controllers using standard OPC interface |
| spellingShingle |
On-line method for optimal tuning of PID controllers using standard OPC interface Algoritmos genéticos Sintonización automática Optimización Controlador PID Genetic algorithms Automatic tuning Optimization PID controller |
| title_short |
On-line method for optimal tuning of PID controllers using standard OPC interface |
| title_full |
On-line method for optimal tuning of PID controllers using standard OPC interface |
| title_fullStr |
On-line method for optimal tuning of PID controllers using standard OPC interface |
| title_full_unstemmed |
On-line method for optimal tuning of PID controllers using standard OPC interface |
| title_sort |
On-line method for optimal tuning of PID controllers using standard OPC interface |
| dc.creator.fl_str_mv |
Riano , Cristhian Díaz-Rodríguez, Jorge Luis Mejía Bugallo, Diego Armando |
| dc.contributor.author.none.fl_str_mv |
Riano , Cristhian Díaz-Rodríguez, Jorge Luis Mejía Bugallo, Diego Armando |
| dc.subject.proposal.spa.fl_str_mv |
Algoritmos genéticos Sintonización automática Optimización Controlador PID |
| topic |
Algoritmos genéticos Sintonización automática Optimización Controlador PID Genetic algorithms Automatic tuning Optimization PID controller |
| dc.subject.proposal.eng.fl_str_mv |
Genetic algorithms Automatic tuning Optimization PID controller |
| description |
Introducción— El controlado PID es el algoritmo matemático mayormente utilizado como estrategia de control regulatorio en entornos industriales. Las aplicaciones son variadas; sin embargo, su respuesta depende del cálculo adecuado de sus tres parámetros: el proporcional, el derivativo y el integral. La sintonización analítica y algunos métodos experimentales resuelven el problema, pero ahora, dentro del contexto digital y de integración de procesos se habilitan nuevas posibilidades de sintonización. Objetivo— Obtener de manera automática y remota los parámetros óptimos del controlador PID aprovechando una conexión online vía el protocolo de comunicación OPC para analizar la respuesta transitoria del sistema. Metodología— El estudio se realiza en tres grandes fases, se inicia con un proceso térmico PD3 SMAR con conexión vía OPC, en esta fase se construye analíticamente el modelo matemático del proceso basado en leyes fundamentales. En la segunda fase utilizando un método analítico de sintonización se crea la arquitectura de control PID sobre la cual se realiza la experimentación online. En la tercera fase se implementan los algoritmos genéticos para sintonización automática, extrayendo medidas de rendimiento del controlador PID a través de la respuesta transitario del proceso y se determinar de manera óptima los valores para los parámetros proporcional, derivativo e integral. Resultados— El método de sintonización automática fue probado con dos procesos industriales correctamente instrumentados y se puedo observar el potencial de aplicación por su buen resultado además de que no se requiere de conocimientos matemáticos específicos en comparación con métodos convencionales de sintonización. Conclusiones— El método de sintonización automática consigue ser empleado de forma remota para calcular los parámetros óptimos de un controlador PID. Los parámetros son calculados a partir de la respuesta transitoria y de la definición de unos criterios de diseño adaptables a cualquier necesidad de control, de respuesta y de proceso. |
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2022 |
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2022 |
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2023-04-11T15:55:00Z |
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2023-04-11T15:55:00Z |
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C. Riaño Jaimes, J. Diaz Rodríguez & D. Mejía Bugallo, “On-line method for optimal tuning of PID controllers using standard OPC interface”, INGECUC, vol. 18, no. 2, pp. 13–26. DOI: http://doi.org/10.17981/ingecuc.18.2.2022.02 |
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2382-4700 |
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https://hdl.handle.net/11323/9981 |
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10.17981/ingecuc.18.2.2022.02 |
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0122-6517 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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C. Riaño Jaimes, J. Diaz Rodríguez & D. Mejía Bugallo, “On-line method for optimal tuning of PID controllers using standard OPC interface”, INGECUC, vol. 18, no. 2, pp. 13–26. DOI: http://doi.org/10.17981/ingecuc.18.2.2022.02 2382-4700 10.17981/ingecuc.18.2.2022.02 0122-6517 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/9981 https://repositorio.cuc.edu.co/ |
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eng |
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INGE CUC |
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[1] R. Borase, D. Maghade, S. Sondkar & S. Pawar, “A review of PID control, tuning methods and applications,” Int. J. Dyn. Control, vol. 9, no. 5, pp. 818–827, Jul. 2020. https://doi.org/10.1007/s40435-020-00665-4 [2] V. Dubey, H. Goud & P. Sharma, “Role of PID Control Techniques in Process Control System: A Review,” in P. Nanda, V. K. Verma, S. Srivastava, R. K. Gupta & A. P. Mazumdar (eds), Data Engineering for Smart Systems. Lectures Notes in Networks and Systems, JAI, IN: Springer, 2021, pp. 659–670. https://doi. org/10.1007/978-981-16-2641-8_62 [3] S. Albatran, I. Smadi & H. Bataineh, “Generalized optimal and explicit PI/PID tuning formulas for underdamped second-order systems,” Int. J. Control Autom. Syst., vol. 18, no. 6, pp. 1023–1032, Nov. 2019. https:// doi.org/10.1007/s12555-019-0178-2 [4] A. Bagis, “Determination of the PID controller parameters by modified genetic algorithm for improved performance,” J. Inf. Sci. Eng., vol. 23, no. 5, pp. 1469–1480, Sep. 2007. https://jise.iis.sinica.edu.tw/JISESearch/pages/View/PaperView.jsf?keyId=50_864 [5] Y. Chen, Y. Ma & W. Yun, “Application of improved genetic algorithm in PID controller parameters optimization,” TELKOMNIKA, vol. 11, no. 3, pp. 1524–1530, Jan. 2013. https://doi.org/10.11591/telkomnika. v11i3.2301 [6] C. Huang & Y. Bai, “PID Tuning of Networked Feedforward-Feedback Control Systems,” in Z. Hou (eds), Lecture Notes in Electrical Engineering. Measuring Technology and Mechatronics Automation in Electrical Engineering, NYC, NY, USA: Springer, 2012, pp. 369–376. https://doi.org/10.1007/978-1-4614-2185-6_45 [7] S. Saxena & Y. Hote, “Internal model control based PID tuning using first-order filter,” Int. J. Control Autom. Syst., vol. 15, no. 1, pp. 149–159, Dec. 2016. https://doi.org/10.1007/s12555-015-0115-y [8] Y. Mitsukura, T. Yamamoto & M. Kaneda, “A design of self-tuning PID controllers using a genetic algorithm,” presented at 1999 American Control Conference-ACC, SD, CA, USA, 2-4 Jun. 1999. https://doi. org/10.1109/ACC.1999.783590 [9] K. Amuthambigaiyin Sundari & P. Maruthupandi, “Optimal Design of PID Controller for the analysis of Two TANK System Using Metaheuristic Optimization Algorithm,” J. Electr. Eng. Technol., vol. 17, no. 1, pp. 627–640, Sep. 2021. https://doi.org/10.1007/s42835-021-00891-6 [10] E. Jove, H. Alaiz-Moretón, I. García-Rodríguez, C. Benavides-Cuellar, J. Casteleiro-Roca, & J. Calvo-Rolle, “PID-ITS: an intelligent tutoring system for PID tuning learning process,” presented at International Joint Conference SOCO’17-CISIS’17-ICEUTE’17, LEO, ES, 6-8 Sept. 2017. https://doi.org/10.1007/978-3- 319-67180-2_71 [11] Z. Bingul, “A new PID tuning technique using differential evolution for unstable and integrating processes with time delay,” presented at International Conference on Neural Information Processing-ICONIP, CCU, IN, 22-25 Nov. 2004. https://doi.org/10.1007/978-3-540-30499-9_38 [12] M. Gani, M. Islam & M. Ullah, “Optimal PID tuning for controlling the temperature of electric furnace by genetic algorithm,” SN Appl. Sci., vol. 1, no. 8, pp. 1–8, Jul. 2019. https://doi.org/10.1007/s42452-019-0929-y [13] M. Mavrinac, Z. iCar, M. Šercer & I. Lorencin, “Genetic Algorithm-Based Parametrization of a PI Controller for DC Motor Control,” Tehn. Glas., vol. 16, no. 1, pp. 16–22, Feb. 2022. https://doi.org/10.31803// tg-20201119185015 [14] S. Kodali, R. Mandava & B. Rao, “Development of an Optimal PID Controller for the 4-DOF Manipulator Using Genetic Algorithm,” in R. Agrawal, J. K. Jain, V. S. Yadav, V. K. Manupati, L. Varela (eds), Recent Advances in Industrial Production. Lecture Notes in Mechanical Engineering, GBG, SE: Springer, 2021, pp. 23–32. https://doi.org/10.1007/978-981-16-5281-3_3 [15] D. Shan, C. Li, X. Qiu & W. Wei, “PID Parameters Tuning Based on Self-Adaptive Hybrid Particle Swarm Optimization Algorithm,” in Z. Zhong (eds), Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012. Lecture Notes in Electrical Engineering, CQ, CN: Springer, 2013, pp. 751–757. https://doi.org/10.1007/978-1-4471-4847-0_92 [16] S. Singh & N. Katal, “Optimal tuning of PID controller for coupled tank liquid level control system using particle swarm optimization,” presented at Sixth International Conference on Soft Computing for Problem Solving-SocProS 2016, PTL, IN, 23-24 Dec. 2016. https://doi.org/10.1007/978-981-10-3325-4_8 [17] C. Sravan Bharadwaj, T. Sudhakar Babu & N. Rajasekar, “Tuning PID controller for inverted pendulum using genetic algorithm,” in, A. Konkani, R. Bera & S. Paul (eds), Advances in Systems, Control and Automation, SG: Springer, 2018, p. 395–404. https://doi.org/10.1007/978-981-10-4762-6_38 [18] N. Thomas & D. P. Poongodi, “Position control of DC motor using genetic algorithm based PID controller,” presented at World Congress on Engineering, WCE 2019, LDN, UK, 3-5 Jul. 2019. http://www.iaeng.org/ publication/WCE2019/ [19] J. C. Tudon-Martínez, J. d.-J. Lozoya-Santos, A. Cantu-Perez & A. Cardenas-Romero, “Advanced Temperature Control Applied on An Industrial Box Furnace,” J. Therm. Sci. Eng. Appl., vol. 14, no. 6, pp. 061001- 1–061001-14, Jun. 2022. https://doi.org/10.1115/1.4052020 |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)Derechos de autor 2022 INGE CUChttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Riano , Cristhian Díaz-Rodríguez, Jorge LuisMejía Bugallo, Diego Armando2023-04-11T15:55:00Z2023-04-11T15:55:00Z2022C. Riaño Jaimes, J. Diaz Rodríguez & D. Mejía Bugallo, “On-line method for optimal tuning of PID controllers using standard OPC interface”, INGECUC, vol. 18, no. 2, pp. 13–26. DOI: http://doi.org/10.17981/ingecuc.18.2.2022.022382-4700https://hdl.handle.net/11323/998110.17981/ingecuc.18.2.2022.020122-6517Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Introducción— El controlado PID es el algoritmo matemático mayormente utilizado como estrategia de control regulatorio en entornos industriales. Las aplicaciones son variadas; sin embargo, su respuesta depende del cálculo adecuado de sus tres parámetros: el proporcional, el derivativo y el integral. La sintonización analítica y algunos métodos experimentales resuelven el problema, pero ahora, dentro del contexto digital y de integración de procesos se habilitan nuevas posibilidades de sintonización. Objetivo— Obtener de manera automática y remota los parámetros óptimos del controlador PID aprovechando una conexión online vía el protocolo de comunicación OPC para analizar la respuesta transitoria del sistema. Metodología— El estudio se realiza en tres grandes fases, se inicia con un proceso térmico PD3 SMAR con conexión vía OPC, en esta fase se construye analíticamente el modelo matemático del proceso basado en leyes fundamentales. En la segunda fase utilizando un método analítico de sintonización se crea la arquitectura de control PID sobre la cual se realiza la experimentación online. En la tercera fase se implementan los algoritmos genéticos para sintonización automática, extrayendo medidas de rendimiento del controlador PID a través de la respuesta transitario del proceso y se determinar de manera óptima los valores para los parámetros proporcional, derivativo e integral. Resultados— El método de sintonización automática fue probado con dos procesos industriales correctamente instrumentados y se puedo observar el potencial de aplicación por su buen resultado además de que no se requiere de conocimientos matemáticos específicos en comparación con métodos convencionales de sintonización. Conclusiones— El método de sintonización automática consigue ser empleado de forma remota para calcular los parámetros óptimos de un controlador PID. Los parámetros son calculados a partir de la respuesta transitoria y de la definición de unos criterios de diseño adaptables a cualquier necesidad de control, de respuesta y de proceso.Introduction— The controlled PID is the most widely used mathematical algorithm as a regulatory control strategy in industrial environments. The applications are varied; however, its answer depends on the proper calculation of its three parameters: the proportional, the derivative, and the integral. Analytical tuning and experimental methods solve the problem, but new tuning possibilities are now enabled within the digital and process integration context. Objective— Automatically and remotely obtain the optimal parameters of the PID controller, taking advantage of an online connection via the OPC communication protocol to analyze the transient response of the system. Methodology— The study is carried out in three main phases; it begins with a PD3 SMAR thermal process with connection via OPC; in this phase, the mathematical model of the process is built analytically based on fundamental laws. In the second phase, using an analytical tuning method, the PID control architecture is created on which the online experimentation is carried out. In the third phase, the genetic algorithms for automatic tuning are implemented, extracting performance measures from the PID controller through the transient response of the process and optimally determining the values for the proportional, derivative, and integral parameters. Results— The automatic tuning method was tested with two properly instrumented industrial processes. The potential for application can be seen due to its good result and because it does not require specific mathematical knowledge compared to conventional tuning methods. Conclusions— The automatic tuning method can be used remotely to calculate the optimal parameters of a PID controller. The parameters are calculated from the transient response and the definition of design criteria adaptable to any need for control, response, and process.14 páginasapplication/pdfengCorporación Universidad de la CostaColombiahttps://revistascientificas.cuc.edu.co/ingecuc/article/view/4152On-line method for optimal tuning of PID controllers using standard OPC interfaceMétodo on-line para sintonización óptima de controladores PID utilizando interface estándar OPCArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85INGE CUC[1] R. Borase, D. Maghade, S. Sondkar & S. Pawar, “A review of PID control, tuning methods and applications,” Int. J. Dyn. Control, vol. 9, no. 5, pp. 818–827, Jul. 2020. https://doi.org/10.1007/s40435-020-00665-4[2] V. Dubey, H. Goud & P. Sharma, “Role of PID Control Techniques in Process Control System: A Review,” in P. Nanda, V. K. Verma, S. Srivastava, R. K. Gupta & A. P. Mazumdar (eds), Data Engineering for Smart Systems. Lectures Notes in Networks and Systems, JAI, IN: Springer, 2021, pp. 659–670. https://doi. org/10.1007/978-981-16-2641-8_62[3] S. Albatran, I. Smadi & H. Bataineh, “Generalized optimal and explicit PI/PID tuning formulas for underdamped second-order systems,” Int. J. Control Autom. Syst., vol. 18, no. 6, pp. 1023–1032, Nov. 2019. https:// doi.org/10.1007/s12555-019-0178-2[4] A. Bagis, “Determination of the PID controller parameters by modified genetic algorithm for improved performance,” J. Inf. Sci. Eng., vol. 23, no. 5, pp. 1469–1480, Sep. 2007. https://jise.iis.sinica.edu.tw/JISESearch/pages/View/PaperView.jsf?keyId=50_864[5] Y. Chen, Y. Ma & W. Yun, “Application of improved genetic algorithm in PID controller parameters optimization,” TELKOMNIKA, vol. 11, no. 3, pp. 1524–1530, Jan. 2013. https://doi.org/10.11591/telkomnika. v11i3.2301[6] C. Huang & Y. Bai, “PID Tuning of Networked Feedforward-Feedback Control Systems,” in Z. Hou (eds), Lecture Notes in Electrical Engineering. Measuring Technology and Mechatronics Automation in Electrical Engineering, NYC, NY, USA: Springer, 2012, pp. 369–376. https://doi.org/10.1007/978-1-4614-2185-6_45[7] S. Saxena & Y. Hote, “Internal model control based PID tuning using first-order filter,” Int. J. Control Autom. Syst., vol. 15, no. 1, pp. 149–159, Dec. 2016. https://doi.org/10.1007/s12555-015-0115-y[8] Y. Mitsukura, T. Yamamoto & M. Kaneda, “A design of self-tuning PID controllers using a genetic algorithm,” presented at 1999 American Control Conference-ACC, SD, CA, USA, 2-4 Jun. 1999. https://doi. org/10.1109/ACC.1999.783590[9] K. Amuthambigaiyin Sundari & P. Maruthupandi, “Optimal Design of PID Controller for the analysis of Two TANK System Using Metaheuristic Optimization Algorithm,” J. Electr. Eng. Technol., vol. 17, no. 1, pp. 627–640, Sep. 2021. https://doi.org/10.1007/s42835-021-00891-6[10] E. Jove, H. Alaiz-Moretón, I. García-Rodríguez, C. Benavides-Cuellar, J. Casteleiro-Roca, & J. Calvo-Rolle, “PID-ITS: an intelligent tutoring system for PID tuning learning process,” presented at International Joint Conference SOCO’17-CISIS’17-ICEUTE’17, LEO, ES, 6-8 Sept. 2017. https://doi.org/10.1007/978-3- 319-67180-2_71[11] Z. Bingul, “A new PID tuning technique using differential evolution for unstable and integrating processes with time delay,” presented at International Conference on Neural Information Processing-ICONIP, CCU, IN, 22-25 Nov. 2004. https://doi.org/10.1007/978-3-540-30499-9_38[12] M. Gani, M. Islam & M. Ullah, “Optimal PID tuning for controlling the temperature of electric furnace by genetic algorithm,” SN Appl. Sci., vol. 1, no. 8, pp. 1–8, Jul. 2019. https://doi.org/10.1007/s42452-019-0929-y[13] M. Mavrinac, Z. iCar, M. Šercer & I. Lorencin, “Genetic Algorithm-Based Parametrization of a PI Controller for DC Motor Control,” Tehn. Glas., vol. 16, no. 1, pp. 16–22, Feb. 2022. https://doi.org/10.31803// tg-20201119185015[14] S. Kodali, R. Mandava & B. Rao, “Development of an Optimal PID Controller for the 4-DOF Manipulator Using Genetic Algorithm,” in R. Agrawal, J. K. Jain, V. S. Yadav, V. K. Manupati, L. Varela (eds), Recent Advances in Industrial Production. Lecture Notes in Mechanical Engineering, GBG, SE: Springer, 2021, pp. 23–32. https://doi.org/10.1007/978-981-16-5281-3_3[15] D. Shan, C. Li, X. Qiu & W. Wei, “PID Parameters Tuning Based on Self-Adaptive Hybrid Particle Swarm Optimization Algorithm,” in Z. Zhong (eds), Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012. 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Appl., vol. 14, no. 6, pp. 061001- 1–061001-14, Jun. 2022. https://doi.org/10.1115/1.40520202613218Algoritmos genéticosSintonización automáticaOptimizaciónControlador PIDGenetic algorithmsAutomatic tuningOptimizationPID controllerPublicationORIGINALOn-line method for optimal tuning of PID controllers using standard OPC interface.pdfOn-line method for optimal tuning of PID controllers using standard OPC interface.pdfArtículoapplication/pdf2055020https://repositorio.cuc.edu.co/bitstreams/0453c47f-1600-421a-b2e8-8dc076ddb506/downloade44e573a59d48d3ffeeaa3ef7726537bMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/b8582f40-9106-4224-a50a-cc19b163d202/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTOn-line method for optimal tuning of PID controllers using standard OPC interface.pdf.txtOn-line method for optimal tuning of PID controllers using standard OPC interface.pdf.txtExtracted texttext/plain44441https://repositorio.cuc.edu.co/bitstreams/7395b2e0-17b9-4ed6-94da-e623bf9bc947/download2a7871ded11efb9e9ee1a639b53c4d83MD53THUMBNAILOn-line method for optimal tuning of PID controllers using standard OPC interface.pdf.jpgOn-line method for optimal tuning of PID controllers using standard OPC interface.pdf.jpgGenerated Thumbnailimage/jpeg13546https://repositorio.cuc.edu.co/bitstreams/b14ca64b-fdd2-48d9-acbd-2f7c4e19ac30/download478f8a5ba0ce3f60ce0dc5014ff9791dMD5411323/9981oai:repositorio.cuc.edu.co:11323/99812024-09-17 10:46:37.101https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos de autor 2022 INGE CUCopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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