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Co-simulation Improvement for Uncertain Flexible Robot Arm

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Abstract

Flexible robot arm driven by Brushless DC Motor (BDCM) under uncertainties represents one of the most complex and heterogeneous system. Indeed, the verification phase becomes a great challenge for designers. Avoid and predict risks accurately at earlier stage represents the main purpose of the Computer-Aided Design (CAD) field. This paper treats the case of robotics system for tracking trajectory problem and attempts to improve the verification phase by identifying the most suitable co-simulation technique. For the system analyzed in this paper, the flexible robot arm driven by Brushless DC actuator is verified using the Model In the Loop (MIL) technique, the Software In the Loop (SIL) technique and CODIS + technique. Each one verifies the system according to a particular abstraction level. The performance of each technique is determined by measuring the accuracy and time simulation. Experimental results have revealed that CODIS+ is the most adequate technique for flexible robot arm, outperforming MIL and SIL by 4–5 times.

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References

  1. Robotics Business Review (2019) URL: http://www.Roboticsbusinessreview.com. [Online]. http://www.Roboticsbusinessreview.com/rbr50/companies/Public. Accessed 1 Dec 2019

  2. Yao B, Zhou Z, Wang L, Xu W, Liu Q, Liu A (2018) Sensorless and adaptive admittance control of industrial robot in physical human? Robot interaction. Robot Comput Integr Manuf 51: pp. 158 –168, https://doi.org/10.1016/j.rcim.2017.12.004, http://www.sciencedirect.com/science/article/pii/S0736584517301564. (Online)

  3. Minh PND, Yann Q, Sylvain L, Claire L (2018) Scanner path planning with the control of overlap for part inspection with an industrial robot. Int J Adv Manuf Technol 98(1):629–643. https://doi.org/10.1007/s00170-018-2336-8. (Online)

    Article  Google Scholar 

  4. Tarn T, Bejczy A, Yun X, Li Z (1991) Effect of motor dynamics on nonlinear feedback robot arm control. IEEE Trans on Robot Autom 7(1):114–122

    Article  Google Scholar 

  5. Hedlund A, Lundberg I, Styrud J, Nordvall M, Sj o¨berg R, Groth T (2018) Method and system for aligning a tool during programming of an industrial robot. U.S. Patent 20 180 373 232A1, Dec. 27, 2018

  6. Good M, Sweet L, Strobei K (1985) Dynamic models for control system design of integrated robot and drive systems. J Dyn Syst Meas Contr 107:53–59

    Article  Google Scholar 

  7. Brogliato B, Ortega R, Lozano R (1995) Global tracking controllers for flexible-joint manipulators: a comparative study. Automatica 31(7):941–956

    Article  MathSciNet  Google Scholar 

  8. Kahraman A, Vijayakar S (2001) Effect of internal gear flexibility on the quasi-static behavior of a planetary gear set. J Mech Des 123:408–415

    Article  Google Scholar 

  9. Sweet L, Good M (1984) Re-definition of the robot motion control problem: Effects of plant dynamics, drive system constraints, and user requirements. In: The 23rd IEEE conference on decision and control, pp 724–732, 1984

  10. Boukezzoula R (2000) Commande floue d’une classe de systèmes non linéaires: application au problème de suivi de trajectoire. Ph.D. dissertation, Université de Chambéry, 2000

  11. Slotine J, Li W (1991) Applied nonlinear control, vol 461. Prentice Hall publisher, Englewood Cliffs, NJ, United States

    MATH  Google Scholar 

  12. Yen VT, Nan WY, Van Cuong P (2018) Recurrent fuzzy wavelet neural networks based on robust adaptive sliding mode control for industrial robot manipulators. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3520-3. (Online)

    Article  Google Scholar 

  13. Zoso N (2011) Modélisation, simulation et commande d’un robot parallèle plan à câbles sous-actionné. Master’s thesis, Faculté des sciences et de génie, Université Laval, Quèbec, 2011

  14. Latour T (2006) Modélisation cinématique et dynamique du robot industriel irb2400 de lecam. ISILF, 20, 2006

  15. COLAS F (2007) Synthèse et réglage de lois de commande adaptées aux axes souples en translation, application aux robots cartésiens 3 axes. Ph.D. Thesis, L’école centrale De Lille, 2007

  16. Gomes C, Thule C, Broman D, Larsen PG, Vangheluwe H (2018) Co-simulation: a survey. ACM Comput Surv 51(3):1–49. https://doi.org/10.1145/3179993. (Online)

    Article  Google Scholar 

  17. Law A, Kelton W (1991) Simulation modeling and analysis, 2nd edn. McGrawHill, New York

    MATH  Google Scholar 

  18. Law AM (2014) Simulation Modeling and Analysis, 5th ed., ser. Mcgraw-hill Series in Industrial Engineering and Management. McGraw-Hill Education, 2014. [Online]. http://gen.lib.rus.ec/book/index.php?md5=028d069d632d9ad57697d89e9db7fbab. Accessed 1 Dec 2019

  19. Ayed MB (2013) Environment de co-simulation/emulation des systèmes continus/discrets. Ph.D. Thesis, Sfax university, National Engineering School of Sfax Ecole, November 2013

  20. Thule C, Gomes C, Deantoni J, Larsen PG, Brauer J, Vangheluwe H (2018) Towards the verification of hybrid co-simulation algorithms. In: Mazzara M, Ober I, Salau¨n G (eds) Software technologies: applications and foundations. Springer International Publishing, Cham, pp 5–20

    Chapter  Google Scholar 

  21. Gomes C, Thule C, Deantoni J, Larsen PG, Vangheluwe H (2018) Co-simulation: The past, future, and open challenges. In: Margaria T, Steffen B (eds) Leveraging applications of formal methods, verification and validation. Distributed systems. Springer International Publishing, Cham, pp 504–520

    Chapter  Google Scholar 

  22. Ayed MB, Bouchhima F, Abid M (2018) Codis + : co-simulation environment for heterogeneous systems. J Control Eng Appl Inf 20(1):98–107

    Google Scholar 

  23. Kong X, Yu X, Chan RR, Lee MY (2013) Co-simulation of a marine electrical power system using powerfactory and matlab/Simulink. In: 2013 IEEE Electric ship technologies symposium (ESTS), DOI 10.1109/ESTS.2013.6523712, pp. 62–65, Apr. 2013

  24. Zhang L, Glaß M, Ballmann N, Teich J (2015) Bridging algorithm and ESL design: MATLAB/Simulink model transformation and validation. Springer International Publishing, Cham, pp 189–206. https://doi.org/10.1007/978-3-319-06317-110. (Online)

    Book  Google Scholar 

  25. Tudoret S, Nadjm-Tehrani S, Benveniste A, Strömberg J-E (2000) Co-simulation of hybrid systems: signal-Simulink. Springer Berlin Heidelberg, Berlin, pp 134–151. https://doi.org/10.1007/3-540-45352-013. (Online)

    Book  MATH  Google Scholar 

  26. Sánchez-Solano S, Brox M (2014) Hardware implementation of embedded fuzzy controllers on FPGAs and ASICs. Atlantis Press, Paris, pp 235–253. https://doi.org/10.2991/978-94-6239-082-913. (Online)

    Book  Google Scholar 

  27. Sandre-Hernandez O, Rangel-Magdaleno JJ, Morales-Caporal R (2014) Simulink-hdl co-simulation of direct torque control of a pm synchronous machine based fpga. In: 2014 11th International Conference on electrical engineering, computing science and automatic control (CCE), pp. 1–6, 2014, https://doi.org/10.1109/iceee.2014.6978298

  28. Ou J, Prasanna VK (2005) Matlab/simulink based hardware/software co-simulation for designing using fpga configured soft processors. In: 19th IEEE international parallel and distributed processing symposium, pp 148b–148b, 2005, https://doi.org/10.1109/ipdps.2005.275

  29. Quang NK, Hieu NT, Ha QP (2014) Fpga-based sensorless PMSM speed control using reduced-order extended kalman filters. IEEE Trans Industr Electron 61(12):6574–6582. https://doi.org/10.1109/TIE.2014.2320215

    Article  Google Scholar 

  30. Strasser T, Stifter M, Andrén F, Palensky P (2014) Co-simulation training platform for smart grids. IEEE Trans Power Syst 29(4):1989–1997. https://doi.org/10.1109/TPWRS.2014.2305740

    Article  Google Scholar 

  31. Kung Y-S, Wu M-K, Thi HLB, Jung T-H, Lee F-C, Chen W-C (2014) FPGA-based hardware implementation of arctangent and arccosine functions for the inverse kinematics of robot manipulator. Eng Comput 31(8):1679–1690. https://doi.org/10.1108/EC-11-2012-0290(Online)

    Article  Google Scholar 

  32. Ding NG, Yong J, Gao F, Hu X (2015).Co-simulation of an integrated braking system based on dual-motor drive by using adams and simulink. In: Mechanical, electronic and information technology engineering, ser. applied mechanics and materials, vol. 743, pp. 133–136. Trans Tech Publications, 5 2015, 10.4028/www.scientific.net/AMM.743.133,

  33. Lozano-Hernandez B, Vasquez-Sanjuan JJ, Rangel-Magdaleno JDJ, Pérez LIO (2016) Simulink/psim/active-hdl co-simulation of passivity-based speed control of PMSM. In: 2016 13th International conference on power electronics (CIEP), pp 7–11, Jun. 2016, https://doi.org/10.1109/ciep.2016.7530722

  34. Cigánek J, Kocúr K, Kozák (2016) Advanced methods of controller design: simulation and co-simulation on FPGA. In: 2016 Cybernetics Informatics (KI), pp. 1–6. https://doi.org/10.1109/CYBERI.2016.7438619

  35. Popp A, Sarrazin M, der Auweraer HV, Fodorean D, Birte O, Karoly B, Martis C (2015) Real-time co-simulation platform for electromechanical vehicle applications. In: 2015 9th International Symposium on advanced topics in electrical engineering (ATEE), pp 240–243, May. 2015, https://doi.org/10.1109/ATEE.2015.7133772

  36. Chettibi N, Mellit A (2014) “Fpga-based real time simulation and control of grid-connected photovoltaic systems. Simul Model Pract Theory 43: 34–53, https://doi.org/10.1016/j.simpat.2014.01.004. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1569190X14000057

  37. Esteban Tlelo-Cuautle LGDFA, Rangel-Magdaleno J (2016) Engineering applications of FPGAs: chaotic systems, artificial neural networks, random number generators, and secure communication systems, 1st ed. Springer International Publishing, 2016. [Online]. http://gen.lib.rus.ec/book/index.php?md5=f85edd8af1a3f44ff584c5f2e5fdba31. Accessed 1 Dec 2019

  38. Chhabra S, Jain H, Saini S (2016) Fpga based hardware implementation of automatic vehicle license plate detection system. In: 2016 International Conference on advances in computing, communications and informatics (ICACCI), pp. 1181–1187, 2016 https://doi.org/10.1109/ICACCI.2016.7732205

  39. Sánchez-Solano S, Brox M, del Toro E, Brox P, Baturone I (2013) Model-based design methodology for rapid development of fuzzy controllers on fpgas. IEEE Trans Ind Inf 9(3):1361–1370. https://doi.org/10.1109/TII.2012.2211608

    Article  Google Scholar 

  40. Reyneri LM, Bellei E, Bussolino E, Gregoretti F, Mari L, Renga F (2003) Simulink-based codesign and co-simulation of a common rail injector test bench. J Circ Syst Comput 12 (02): 171–202. https://doi.org/10.1142/s0218126603000738. [Online]. Available: http://www.worldscientific.com/doi/abs/10.1142/S0218126603000738

  41. Zouari L, Abid H, Abid M (2015) Sliding mode and PI controllers for uncertain flexible joint manipulator. Int J Autom Comput 12(2):117–124

    Article  Google Scholar 

  42. Martins-Filho LS, Santana AC, Duarte RO, Junior GA (2014) Processor-in-the-loop simulations applied to the design and evaluation of a satellite attitude control, computational and numerical simulations. InTechOpen publisher, London

    Google Scholar 

  43. Stoeppler G, Menzel T, Douglas S (2005) Hardware-in-the-loop simulation of machine tools and manufacturing systems. Comput Control Eng J 16(1):10–15

    Article  Google Scholar 

  44. Zenati A (2007) Modélisation et simulation de microsystèmes multi domaines à signaux mixtes: vers le prototypage virtuel d’un microsystème autonome. Universite´ Joseph Fourier Grenoble I, 2007

  45. Kis L, Regula G, Lantos B (2008) Design and hardware-in-the-loop test of the embedded control system of an indoor quadrotor helicopter. In: Intelligent solutions in embedded systems, pp 1–10, July 2008

  46. Akpolat Z, Asher G, Clare J (1999) Dynamic emulation of mechanical loads using a vector-controlled induction motor-generator set. IEEE Trans Ind Electron 46(2):370–379

    Article  Google Scholar 

  47. Svenningsen PTJP (2005) Modeling, simulation and implementation of a non-coherent binary-frequency-shift-keying (bfsk) receiver-transmitter into a field programmable gate array (FPGA). Ph.D. dissertation, Monterey, California. Naval Postgraduate School, 2005

  48. Graf C, Maas J, Schulte T, Weise-Emden J (2008) Real-time Hil-simulation of power electronics. In: 34th Annual Conference of IEEE industrial electronics, IECON 2008, pp 2829–2834, 2008

  49. Mauri M (2009) Hardware in the loop simulation of renewable distributed generation systems. InTechOpen publisher., London

    Book  Google Scholar 

  50. Lentijo S, D’Arco S, Monti A (2010) Comparing the dynamic performances of power hardware-in-the-loop interfaces. IEEE Trans Ind Electron 57:1195–1207

    Article  Google Scholar 

  51. Hardware in the Loop from the MATLAB/Simulink Environment, Altera Corporation, September 2013. [Online]. https://www.altera.com/.com. Accessed 1 Dec 2019

  52. Karimi S (2009) Continuity of service for three-phase power converters and prototyping “FPGA in the loop”: application au filtre actif parallèle. Ph.D. Thesis, Henri Poincaré University, Nancy-I, 2009

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Acknowledgement

The authors would like to thank Deanship of Scientific Research at Majmaah University for funding this project under the number R-1441-40.

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Authors and Affiliations

  1. CES Laboratory, Sfax University, Sfax, Tunisia

    Lilia Zouari, Mossaad Ben Ayed & Mohamed Abid

  2. Department of Computer Science, College of Sciences and Humanities Sciences, Majmaah University, Majmaah, Saudi Arabia

    Mossaad Ben Ayed & Shaya Abdullah Alshaya

  3. Electrical Department, College of Engineering, King Faisal University, Al-Ahsa, Saudi Arabia

    Slim Chtourou

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  1. Lilia Zouari
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Correspondence to Lilia Zouari or Mossaad Ben Ayed.

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Zouari, L., Ben Ayed, M., Chtourou, S. et al. Co-simulation Improvement for Uncertain Flexible Robot Arm. J. Electr. Eng. Technol. 15, 367–379 (2020). https://doi.org/10.1007/s42835-019-00330-7

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