Hardware-in-the-loop testing of current cycle feedback ILC for stabilisation and tracking control of under-actuated visual servo system

References

• Abdessameud, A., & Janabi-Sharifi, F. (2015). Image-based tracking control of VTOL unmanned aerial vehicles. Automatica, 53, 111–119. https://doi.org/10.1016/j.automatica.2014.12.032
   Web of Science ®Google Scholar
• Ahn, H.-S., Chen, Y., & Moore, K. L. (2007). Iterative learning control: Brief survey and categorization. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 37(6), 1099–1121. https://doi.org/10.1109/TSMCC.2007.905759
   Web of Science ®Google Scholar
• Allibert, G., Hua, M.-D., Krupínski, S., & Hamel, T. (2019). Pipeline following by visual servoing for autonomous underwater vehicles. Control Engineering Practice, 82, 151–160. https://doi.org/10.1016/j.conengprac.2018.10.004
   Web of Science ®Google Scholar
• Altın, B., Wang, Z., Hoelzle, D. J., & Barton, K. (2018). Robust monotonically convergent spatial iterative learning control: Interval systems analysis via discrete fourier transform. IEEE Transactions on Control Systems Technology, 27(6), 2470–2483. https://doi.org/10.1109/TCST.2018.2868039
   Web of Science ®Google Scholar
• Anis, Y. H., Mills, J. K., & Cleghorn, W. L. (2008). Visual-servoing of a six-degree-of-freedom robotic manipulator for automated microassembly task execution. Journal of Micro/Nanolithography, MEMS, and MOEMS, 7(3). Article ID: 033017. https://doi.org/10.1117/1.2970145
   Web of Science ®Google Scholar
• Armendariz, M. A. M., Olvera, C. A. P., Rodriguez, F. O., & Rubio, E. (2010). Indirect hierarchical FCMAC control for the ball and plate system. Neurocomputing, 73(13), 2454–2463. https://doi.org/10.1016/j.neucom.2010.03.023
   Web of Science ®Google Scholar
• Bristow, D. A., Tharayil, M., & Alleyne, A. G. (2006). A survey of iterative learning control. IEEE Control Systems Magazine, 26(3), 96–114. https://doi.org/10.1109/MCS.2006.1636313
   Web of Science ®Google Scholar
• Chang, W.-C. (2018). Robotic assembly of smartphone back shells with eye-in-hand visual servoing. Robotics and Computer-Integrated Manufacturing, 50, 102–113. https://doi.org/10.1016/j.rcim.2017.09.010
   Web of Science ®Google Scholar
• Das, A., & Roy, P. (2017). Improved performance of cascaded fractional-order SMC over cascaded SMC for position control of a ball and plate system. IETE Journal of Research, 63(2), 238–247. https://doi.org/10.1080/03772063.2016.1258336
   Web of Science ®Google Scholar
• Deng, Y., Wang, J., Li, H., Liu, J., & Tian, D. (2019). Adaptive sliding mode current control with sliding mode disturbance observer for PMSM drives. ISA Transactions, 88, 113–126. https://doi.org/10.1016/j.isatra.2018.11.039
   PubMed Web of Science ®Google Scholar
• Dong, J., & Zhang, J. (2020). A new image-based visual servoing method with velocity direction control. Journal of the Franklin Institute, 357(7), 3993–4007. https://doi.org/10.1016/j.jfranklin.2020.01.012
   Web of Science ®Google Scholar
• Fan, X., Zhang, N., & Teng, S. (2004). Trajectory planning and tracking of ball and plate system using hierarchical fuzzy control scheme. Fuzzy Sets and Systems, 144(2), 297–312. https://doi.org/10.1016/S0165-0114(03)00135-0
   Web of Science ®Google Scholar
• Galvan-Colmenares, S., Moreno-Armendáriz, M. A., Rubio, J. d. J., Ortíz-Rodriguez, F., Yu, W., & Aguilar-Ibáñez, C. F. (2014). Dual PD control regulation with nonlinear compensation for a ball and plate system. Mathematical Problems in Engineering, 2014, Article ID 894209. https://doi.org/10.1155/2014/894209
   Google Scholar
• Ghanbari, V., V. H. Duenas, Antsaklis, P. J., & Dixon, W. E. (2018). Passivity-based iterative learning control for cycling induced by functional electrical stimulation with electric motor assistance. IEEE Transactions on Control Systems Technology, 27(5), 2287–2294. https://doi.org/10.1109/TCST.87
   Web of Science ®Google Scholar
• Helfrich, B. E., Lee, C., Bristow, D. A., Xiao, X. H., Dong, J., Alleyne, A. G., Salapaka, S. M., & Ferreira, P. M. (2010). Combined H∞-feedback control and iterative learning control design with application to nanopositioning systems. IEEE Transactions on Control Systems Technology, 18(2), 336–351. https://doi.org/10.1109/TCST.2009.2018835
   Web of Science ®Google Scholar
• Ho, M. T., Rizal, Y., & Chu, L. M. (2013). Visual servoing tracking control of a ball and plate system: Design, implementation and experimental validation. International Journal of Advanced Robotic Systems, 10(7), 287. https://doi.org/10.c5772/56525
   Google Scholar
• Hua, C., Qiu, Y., & Guan, X. (2020). Enhanced model-free adaptive iterative learning control with load disturbance and data dropout. International Journal of Systems Science, 51(11), 2057–2067. https://doi.org/10.1080/00207721.2020.1784492
   Web of Science ®Google Scholar
• Huang, D., Xu, J. X., Venkataramanan, V., & Huynh, T. C. T. (2013). High-performance tracking of piezoelectric positioning stage using current-cycle iterative learning control with gain scheduling. IEEE Transactions on Industrial Electronics, 61(2), 1085–1098. https://doi.org/10.1109/TIE.2013.2253071
   Web of Science ®Google Scholar
• Hui, Y., Chi, R., Huang, B., & Hou, Z. (2019). Extended state observer-based data-driven iterative learning control for permanent magnet linear motor with initial shifts and disturbances. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/TSMC.2019.2907379
   Web of Science ®Google Scholar
• Kong, F. H., & Manchester, I. R. (2020). Contraction analysis of nonlinear noncausal iterative learning control. Systems & Control Letters, 136, 1–8. https://doi.org/10.1016/j.sysconle.2019.104599
   Web of Science ®Google Scholar
• Kuc, T. Y., Lee, J. S., & Nam, K. (1992). An iterative learning control theory for a class of nonlinear dynamic systems. Automatica, 28(6), 1215–1221. https://doi.org/10.1016/0005-1098(92)90063-L
   Web of Science ®Google Scholar
• Kumaran, S. K., Mohapatra, S., Dogra, D. P., Roy, P. P., & Kim, B.-G. (2019). Computer vision-guided intelligent traffic signaling for isolated intersections. Expert Systems with Applications, 134, 267–278. https://doi.org/10.1016/j.eswa.2019.05.049
   Web of Science ®Google Scholar
• Liu, S., & Dong, J. (2020). Robust online model predictive control for image-based visual servoing in polar coordinates. Transactions of the Institute of Measurement and Control, 42(4), 890–903. https://doi.org/10.1177/0142331219895074
   Web of Science ®Google Scholar
• Liu, Y., Li, J., Zhang, Z., Hu, X., & Zhang, W. (2015). Experimental comparison of five friction models on the same test-bed of the micro stick-slip motion system. Mechanical Sciences, 6(1), 15. https://doi.org/10.5194/ms-6-15-2015
   Web of Science ®Google Scholar
• Ma, J., Tao, H., & Huang, J. (2020). Observer integrated backstepping control for a ball and plate system. International Journal of Dynamics and Control, 1–8. https://doi.org/10.1007/s40435-020-00629-8
   Google Scholar
• Meng, T., & He, W. (2017). Iterative learning control of a robotic arm experiment platform with input constraint. IEEE Transactions on Industrial Electronics, 65(1), 664–672. https://doi.org/10.1109/TIE.2017.2719598
   Web of Science ®Google Scholar
• Mochizuki, S., & Ichihara, H. (2013). Generalized Kalman–Yakubovich–Popov lemma based I-PD controller design for ball and plate system. Journal of Applied Mathematics, 2013, 1–9. https://doi.org/10.1155/2013/854631
   Google Scholar
• Mohammadi, A., & Ryu, J.-C. (2018). Neural network-based PID compensation for nonlinear systems: Ball-on-plate example. International Journal of Dynamics and Control, 8, 178–188. https://doi.org/10.1007/s40435-018-0480-5
   Google Scholar
• Park, J. H., & Lee, Y. J. (2003). Robust visual servoing for motion control of the ball on a plate. Mechatronics, 13(7), 723–738. https://doi.org/10.1016/S0957-4158(02)00039-9
   Web of Science ®Google Scholar
• Sebastian, G. (2019). Feedback-based iterative learning control for constrained systems with application to robotic manipulators [PhD thesis].
   Google Scholar
• Sebastian, G., Tan, Y., & Oetomo, D. (2019). Convergence analysis of feedback-based iterative learning control with input saturation. Automatica, 101, 44–52. https://doi.org/10.1016/j.automatica.2018.11.045
   Web of Science ®Google Scholar
• Shen, D. (2018). Iterative learning control with incomplete information: A survey. IEEE/CAA Journal of Automatica Sinica, 5(5), 885–901. https://doi.org/10.1109/JAS.2018.7511123
   Google Scholar
• Shirzadeh, M., Asl, H. J., Amirkhani, A., & Jalali, A. A. (2017). Vision-based control of a quadrotor utilizing artificial neural networks for tracking of moving targets. Engineering Applications of Artificial Intelligence, 58, 34–48. https://doi.org/10.1016/j.engappai.2016.10.016
   Web of Science ®Google Scholar
• Wang, Y., Sun, M., Wang, Z., Liu, Z., & Chen, Z. (2014). A novel disturbance-observer based friction compensation scheme for ball and plate system. ISA Transactions, 53(2), 671–678. https://doi.org/10.1016/j.isatra.2013.11.011
   PubMed Web of Science ®Google Scholar
• Wu, Y., Zou, Q., & Su, C. (2009). A current cycle feedback iterative learning control approach for AFM imaging. IEEE Transactions on Nanotechnology, 8(4), 515–527. https://doi.org/10.1109/TNANO.2009.2015051
   Web of Science ®Google Scholar
• Xu, J. X., & Tan, Y. (2003). Linear and nonlinear iterative learning control (Vol. 291). Springer.
   Google Scholar
• Xu, J. X., Wang, X. W., & Heng, L. T. (1995). Analysis of continuous iterative learning control systems using current cycle feedback. In Proceedings of 1995 American control conference – ACC'95 (Vol. 6, pp. 4221–4225). IEEE.
   Google Scholar