Cascade Control System Using Feedback From Real-time Digital Image Point Tracking
Source abstract
Abstract
Real-time digital image point tracking feedback was used to control a servo-hydraulic load train during the loading of a Glass Fiber Reinforced Plastic (GFRP) cantilever beam. The loading pattern was defined in terms of two translational and one rotational in-plane degrees-of-freedom at a single control point on the beam. Tests were performed with both single-and multi degree-of-freedom controls, using independent and simultaneous control of the actuators, respectively. A reference control system was set up to transfer the displacements of three hydraulic actuators into a theoretical displacement of the control point using an assumption of rigidity. The discrepancies between this theoretical displacement and the actual displacement were addressed through three control configurations that used a linear transfer function, a feedback signal from the digital image point tracking using Proportional and Integral control, and a combination of these two. Performance was evaluated at frequencies ranging from 0.01 – 1 Hz. The combined implementation of the linear transfer function and digital image point tracking feedback showed the greatest improvement over the reference configuration, reducing the error between the desired and measured signal by 51–93%.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Optical measurement and interference techniques · Advanced Vision and Imaging · Iterative Learning Control Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (21)
Show source-provided references
1. D. A. Vajari, K. Martyniuk, B. F. Sørensen and B. N. Legarth, “Experimental and numerical studies of the micro-mechanical failure in composites,” in 19th International Conference on Composite Materials, Montreal, 2013
2. F. Gara, L. Ragni, D. Roia and L. Dezi, “Experimental tests and numerical modelling of wall sandwich panels,” Elsevier, Eng Struct, vol. 37, pp. 193–204, 201210.1016/j.engstruct.2011.12.027 ↗
3. Jensen FM, Falzon B, Ankersen J, Stang H (2006) Structural testing and numerical simulation of a 34m composite wind turbine blade. Elsevier, Composite Structures 76(1–2):52–6110.1016/j.compstruct.2006.06.008 ↗
4. A. Makris, C. Ramault, D. Van Hemelrijck, E. Lamkanfi and W. Van Paepegem, “BIAXIAL MECHANICAL FATIGUE USING CRUCIFORM COMPOSITE SPECIMENS,” -
5. Laustsen S, Lund E, Kühlmeier L, Thomsen OT (2014) Failure behaviour of grid-scored foam cored composite sandwich panels for wind turbine blades subjected to realistic multiaxial loading conditions. J Sandw Struct Mater 16(5):481–51010.1177/1099636214541367 ↗
6. Xiao C (2017) Experimental investigation on structural collapse of a large composite wind turbine blade under combined bending and torsion. Compos Struct 160:435–44510.1016/j.compstruct.2016.10.086 ↗
7. Waldbjørn JP, Høgh JH, Jacob W-S, Nielsen MW, Branner K, Stang H, Berggreen C (2014) Strain and displacement controls by fibre bragg grating and digital image correlation. Strain 50(3):262–27310.1111/str.12089 ↗
8. Le Flohic J, Parpoil V, Bouissou S, Poncelet M, Leclerc H (2014) A 3D displacement control by digital image correlation for the multiaxial testing of materials. Exp Mech 54(5):817–828
9. Kindermann MR, Wilson AR, Arnott DR (1997) A multiple constant displacement-rate tensile testing machine. Meas Sci Technol 8(4):390–39710.1088/0957-0233/8/4/005 ↗
10. Petiteau J-C, Othman R, Guegan P, Sourne HL, Verron E (2013) Dynamic uniaxial extension of elastomers at constant true strain rate. Elsevier, Polymer Testing 32(2):394–40110.1016/j.polymertesting.2012.10.007 ↗
11. Høgh JH, Waldbjørn JP, Wittrup-Schmidt J, Stang H, Berggreen CC (2015) Quasi-static single-component hybrid simulation of a composite structure with multi-axis control. Wiley-Blackwell, Strain 51(6):459–473
12. I. Elorza and D. P. Stoten, “On alternative control schemes for real-time dynamic substructuring tests,” in UKACC International Conference on CONTROL, Coventry, 201010.1049/ic.2010.0297 ↗
13. J. P. Waldbjoern, A. Quinlan and C. Berggreen, “High precision tracking compensator for single component hybrid simulation,” in The 13th international conference on advances in experiemental mechanics, Southampton, UK, 2018
14. S. Mguil-Touchal, F. Morestin and M. Brunet, “Various experimental applications of digital image correlation method,” in 8th International Conference on Computational Methods and Experimental Measurements, CMEM , Rhodes, 1997
15. Wu R, Kong C, Zhang D (2016) Real-time digital image correlation for dynamic strain measurement. Exp Mech 56(5):833–84310.1007/s11340-016-0133-6 ↗
16. A. Quinlan and J. Waldbjoern, “Single-Component Hybrid Simulation Techniques for Validation of Fatigue Models,” in Proceedings, Brussels, 201810.3390/ICEM18-05328 ↗
17. Fayolle X, Calloch S, Hild F (2007) Controlling testing machines with digital image correlation. Exp Tech 31(3):57–6310.1111/j.1747-1567.2007.00152.x ↗
18. Kanchanomai C, Yamamoto S, Miyashita Y, Mutoh Y, McEvily AJ (2002) Low cycle fatigue test for solders using non-contact digital image measurement system. Inernational Journal of Fatigue 24(1):57–6710.1016/S0142-1123(01)00052-4 ↗
19. Tao G, Xia Z (2005) A non-contact real-time strain measurement and control system for multiaxial cyclic/fatigue tests of polymer materials by digital image correlation method. Polym Test 24(7):844–85510.1016/j.polymertesting.2005.06.013 ↗
20. Jelali M, Kroll A (2003) Hydraulic servo-systems: modelling, identification and control. -(advances in industrial control). Springer, London10.1007/978-1-4471-0099-7 ↗
21. Lee Y, Park S, Lee M (1998) PID controller tuning to obtain desired closed loop responses for Cascade control systems. Ind Eng Chem Res 37(5):1859–186510.1021/ie970769t ↗