影像量測技術廣泛應用於結構工程與地震工程試驗，做為試體行為非接觸量測方法的一種嶄新選擇。在過去的研究中，甚至已應用影像量測技術即時分析試體的位移，並將分析結果透過光纖共享記憶體回饋至油壓伺服致動器，取代傳統接觸式外部位移計，成功地之完成即時非接觸式外部位移控制。然而因影像量測計算之更新頻率遠低於油壓致動器控制訊號之更新頻率，導致階梯式訊號之產生；此外，在追蹤靜止不動之目標時，因產生重複精度之問題，會有影像位移訊號上下以一個像素跳動的現象，進而影響油壓致動器位移控制之表現。有鑑於此，本研究將以解決上述問題為核心目標，並以原影像量測技術系統為基礎，進一步改善影像量測於致動器位移回饋控制之品質。
本研究使用LabVIEW之機器視覺開發軟體所提供之低差異採樣匹配法，並透過增加邊緣搜索與次像素精度演算法之設置，成功解決因重複精度跳動的問題。除此之外，為了更進一步提升影像量測回饋訊號之更新頻率，本研究提出中介層演算法的方式，將影像量測計算之更新頻率提升十倍，達成更有效之即時位移回饋控制。最後，將本研究所提出的方法安裝於結構實驗室中進行驗證，實驗結果顯示，在靜態反覆載重實驗以及動態位移加載實驗中，皆使油壓致動器位移表現較過去更加精準且穩定，未來應用於真實大型結構試驗中之可行性因此大幅提升。此外，本研究同時將此技術應用於材料拉伸試驗，以萬能材料試驗機進行無接觸式之影像量測應變回饋控制，顯示本研究所開發之即時影像量測回饋控制技術應用之多樣性。

Image-based analysis has been applied to various experiments for structural engineering and earthquake engineering studies in the past decade. Recently, former research has successfully utilized image analysis for real-time displacement feedback control of servo-hydraulic actuators for structural testing. However, a step-like signal was generated because the analysis rate of image measurement was much lower than the control rate of the hydraulic actuator. Meanwhile, the actuator displacement could tremble when it was holding its position due to the repeatability of the camera and image analysis. Accordingly, the displacement tracking performance of actuator became inferior.
In this study, low-discrepancy sampling matching method is used for real-time image analysis for decreasing the elapsed time of computation. The problem of repeatability is solved by adding edge detection and subpixel accuracy algorithm. Furthermore, the image analysis rate is increased up to ten times by introducing a middleware which aims to predict the implicit displacement between each two displacement measurements. Consequently, a more effective real-time displacement feedback control can be achieved. Finally, experimental verification is conducted in the small-scale structural laboratory. Experimental results demonstrate that the displacement tracking control performance of the actuator becomes more stable and accurate in quasi-static cyclic loading tests and dynamic-loading tests. In addition, the technology is also applied to material tensile testing for strain feedback control. Experimental results reveal that strain feedback control of the universal testing machine can be achieved successfully. The proposed image-based feedback control method for servo-hydraulic actuators has great potential for real application of structural testing in the future.

[1] 鄭宇辰(2020)。應用影像量測技術於致動器位移回饋控制之研究。國立臺灣科技大學營建工程系碩士論文，台北市。
[2] Kan, W., Krogmeier, J., and Doerschuk, P. (1996) “Model-based vehicle tracking from image sequences with an application to road surveillance” Optical Engineering. 35(6), 1723-1729.
[3] Weerasinghe, I. T., Ruwanpura, J. Y., Boyd, J. E., and Habib, A. F. (2012) “Application of Microsoft Kinect sensor for tracking construction workers” ASCE. Construction Research Congress, Reston, VA, 858–867.
[4] Wen, Y., Lu, Y., Yan, J., Zhou, Z., von Deneen, K. M., and P. Shi (2011) “An algorithm for license plate recognition applied to intelligent transportation system” IEEE Transactions on Intelligent Transportation Systems. 12(3), 830-845
[5] Woods, J.E., Yang, Y.S., Chen, P.C., Lau, D. T., and Erochko, J. (2021) “Automated crack detection and damage index calculation for RC structures using image analysis and fractal dimension” ASCE Journal of Structural Engineering. 147(4): 04021019.
[6] Benhimane, S., and Malis, E. (2004) “Real-time image-based tracking of planes using efficient second-order minimization” IEEE/RSJ international conference on intelligent robots and systems.
[7] Malis, E. (2004) “Improving vision-based control using efficient second-order minimization techniques” IEEE ICRA. 1843-1848.
[8] Sutton, M.A., McNeill, S.R., Helm, J.D., and Chao, Y.J. (2000) “Advances in two-dimensional and three-dimensional computer vision” Topics in Applied Physics (1st ed.). Rastogi, P. K. (Ed.), Berlin:Springer. 323-372.
 
[9] Pan, B., Xie, H. Wang, Z., Qian, K., and Wang, Z. (2008) “Study on subset size selection in digital image correlation for speckle patterns” Opt. Express 16(10), 7037-7048.
[10] Gao, Y., Cheng, T., Su, Y., Xu, X., Zhang, Y., and Zhang, Q. (2015) “High-efficiency and high-accuracy digital image correlation for three-dimensional measurement” Optics and Lasers in Engineering 65, 73-80.
[11] Bruck, H.A., McNeill, S.R., Sutton, M.A. and Peters III, W.H. (1989) “Digital image correlation using Newton-Raphson method of partial differential correction” Experimental Mechanics 29, 261-267.
[12] Pilch, A., Mahajan, A., and Chu, T. (2004) “Measurement of whole-field surface displacements and strain using a genetic algorithm based intelligent image correlation method” Journal of Dynamic Systems Measurement and Control 126, 479-488.
[13] Li, L., Chen, Y., Yu, X., Liu, R., and Huang, C., (2015) “Sub-pixel flood inundation mapping from multispectral remotely sensed images based on discrete particle swarm optimization” ISPRS Journal of Photogrammetry and Remote Sensing 101, 10–21.
[14] Guizar-Sicairos, M., Thurman, S.T., and Fienup, J.R. (2008) “Efficient subpixel image registration algorithms” Optics Letters 33(2), 156-158.
[15] Ma, C., Ren, Q., and Zhao, J. (2021) “Optical-numerical method based on a convolutional neural network for full-field subpixel displacement measurements” Optics Express 29, 9137-9156.
[16] Tao, G., and 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” Polymer Test 24(7), 844-855.
 

[17] Waldbjoern, J., Quinlan, A., Stang, H., and Berggreen, C. (2022) “Cascade Control System Using Feedback From Real-time Digital Image Point Tracking” Experimental Techniques 46, 203-212.
[18] Chen, P.C., Yang, Y.S., and Cheng, Y.C. (2022) “Displacement Feedback Control of Actuators for Structural Testing using Image Processing and Analysis” Earthquake Engineering and Structural Dynamics 51(3), 630-647.
[19] Chen, P. C., Ting, G. C., and Li, C. H. (2020) “A versatile small-scale structural laboratory for novel experimental earthquake engineering” Earthquakes and Structures 18(3), 337-348.
[20] Kim, S. B., Spencer Jr, B. F., and Yun, C. B. (2005) “Frequency domain identification of multi-input, multi-output systems considering physical relationships between measured variables” Journal of Engineering Mechanics 131(5), 461-472.
[21] National Instruments (2000) “IMAQ Vision Concepts Manual” National Instruments Corporation, 1-313