Abstract
This study presents a cutting-edge approach to design iterative learning control (ILC) in micro-robotic deposition systems, utilizing nonlinear time-frequency analysis through adaptive Fourier decomposition (AFD). While ILC has demonstrated its effectiveness in achieving precise trajectory tracking, achieving a balance between robustness and convergence can be challenging. To address this challenge, we introduce a novel nonlinear time-frequency ILC design from a signal processing perspective, which exploits an advanced version of Fourier decomposition called AFD. By employing adaptive basis functions, AFD enables fast energy convergence during the control process. To reduce noise amplification and system delay, we propose a phase-lead ILC algorithm with zero amplitude attenuation. Additionally, we introduce a tunable bandwidth L-Q filter to achieve an optimal trade-off between robustness and convergence. The filter’s bandwidth is adaptively adjusted based on the frequency content of the system, with a narrower bandwidth for low-frequency signals to accelerate convergence and a wider bandwidth for high-frequency signals to enhance robustness. Simulation results demonstrate the exceptional performance of the proposed ILC design in a micro-robotic deposition system.
Similar content being viewed by others
Data Availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
References
Wu, Y., Zou, Q.: An iterative-based feedforward-feedback control approach to high-speed atomic force microscope imaging. J. Dyn. Syst. Meas. Control 131(6), 061105 (2009)
Fung, R.-F., Huang, J.-S., Chien, C.-G., Wang, Y.-C.: Design and application of a continuous repetitive controller for rotating mechanisms. Int. J. Mech. Sci. 42(9), 1805–1819 (2000)
Li, M., Kaiming Yang, Yu., Zhu, H.M., Chuxiong, H.: State/model-free variable-gain discrete sliding mode control for an ultraprecision wafer stage. IEEE Trans. Ind. Electron. 64(8), 6695–6705 (2016)
Fengyu, Z., Yugang, W.: Iterative learning control for fractional order nonlinear system with initial shift. Nonlinear Dyn. 106(4), 3305–3314 (2021)
Yan, Y., Wang, H., Zou, Q.: A decoupled inversion-based iterative control approach to multi-axis precision positioning: 3D nanopositioning example. Automatica 48(1), 167–176 (2012)
Duan, M., Yoon, D., Okwudire, C.E.: A limited-preview filtered B-spline approach to tracking control-with application to vibration-induced error compensation of a 3D printer. Mechatronics 56, 287–296 (2018)
Yu, X., Hou, Z., Polycarpou, M.M.: Distributed data-driven iterative learning consensus tracking for nonlinear discrete-time multi-agent systems. IEEE Trans. Autom. Control 67(7), 3670–3677 (2022)
Panpan, G., Tian, S.: Consensus tracking control via iterative learning for singular multi-agent systems. IET Control Theory Appl. 13(11), 1603–1611 (2019)
Ge, Yu., Sheng, Z., Fang, Y., Zhang, L.: An AFD-based ILC dynamics adaptive matching method in frequency domain for distributed consensus control of unknown multiagent systems. IEEE Trans. Circuits Syst. I Regul. Pap. 69(8), 3366–3378 (2022)
Chi, R., Hui, Y., Wang, R., Huang, B., Hou, Z.: Discrete-time-distributed adaptive ILC with nonrepetitive uncertainties and applications to building HVAC systems. IEEE Trans. Syst. Man Cybern.: Syst. 52(8), 5068–5080 (2022)
Chi, R., Lv, Y., Huang, B.: Distributed iterative learning temperature control for multi-zone HVAC system. J. Frankl. Inst. 357(2), 810–831 (2020)
Yuan, H., Huang, D., Li, X.: Adaptive speed tracking control for high speed trains under stochastic operation environments. Automatica 147, 110674 (2023)
Qiongxia, Yu., Hou, Z., Jian-Xin, X.: D-type ILC based dynamic modeling and norm optimal ILC for high-speed trains. IEEE Trans. Control Syst. Technol. 26(2), 652–663 (2017)
Gorinevsky, D.: Loop shaping for iterative control of batch processes. IEEE Control Syst. Mag. 22(6), 55–65 (2002)
Ge, X., Stein, J.L., Ersal, T.: Frequency-domain analysis of robust monotonic convergence of norm-optimal iterative learning control. IEEE Trans. Control Syst. Technol. 26(2), 637–651 (2017)
Van de Wijdeven, J., Donkers, T., Bosgra, O.: Iterative learning control for uncertain systems: robust monotonic convergence analysis. Automatica 45(10), 2383–2391 (2009)
Ge, X., Stein, J.L., Ersal, T.: Optimality of norm-optimal iterative learning control among linear time invariant iterative learning control laws in terms of balancing robustness and performance. J. Dyn. Syst. Meas. Control 141(4), 044502 (2019)
De Roover, D., Bosgra, O.H.: Synthesis of robust multivariable iterative learning controllers with application to a wafer stage motion system. Int. J. Control 73(10), 968–979 (2000)
Rotariu, I., Steinbuch, M., Ellenbroek, R.: Adaptive iterative learning control for high precision motion systems. IEEE Trans. Control Syst. Technol. 16(5), 1075–1082 (2008)
Rotariu, I., Vullings, E.: Multi-dictionary matching pursuit for servo error analysis applied to iterative learning control. In: IEEE International Workshop on Intelligent Signal Processing, pp. 86–91. IEEE (2005)
Zhang, B., Wang, D., Ye, Y.: Wavelet transform-based frequency tuning ILC. IEEE Trans. Syst., Man, Cybern., Part B (Cybern.) 35(1), 107–114 (2005)
Zhu, Q., Jian-Xin, X., Huang, D., Guang-Di, H.: Iterative learning control for linear discrete-time systems with unknown high-order internal models: a time-frequency analysis approach. Asian J. Control 20(1), 370–385 (2018)
Mishra, S., Coaplen, J., Tomizuka, M.: Precision positioning of wafer scanners segmented iterative learning control for nonrepetitive disturbances [applications of control]. IEEE Control Syst. Mag. 27(4), 20–25 (2007)
Wang, Z., Wong, C.M., Rosa, A., Qian, T., Wan, F.: Adaptive Fourier decomposition for multi-channel signal analysis. IEEE Trans. Signal Process. 70, 903–918 (2022)
Wu, J., Shu, H., Wang, L., Senhadji, L.: Fast algorithms for the computation of sliding sequency-ordered complex Hadamard transform. IEEE Trans. Signal Process. 58(11), 5901–5909 (2010)
Dai, L., Zhang, L.: A joint spatiotemporal video compression based on stochastic adaptive Fourier decomposition. IEEE Signal Process. Lett. 29, 1531–1535 (2022)
Tenreiro Machado, J., Duarte, F.B., Duarte, G.M.: Analysis of financial data series using fractional Fourier transform and multidimensional scaling. Nonlinear Dyn. 65, 235–245 (2011)
Qian, T., Zhang, L., Li, Z.: Algorithm of adaptive Fourier decomposition. IEEE Trans. Signal Process. 59(12), 5899–5906 (2011)
Zhang, L.: Adaptive Fourier decomposition based time-frequency analysis. J. Electron. Sci. Technol. 12(2), 201–205 (2014)
Zhang, L., Qian, T., Mai, W., Dang, P.: Adaptive Fourier decomposition-based Dirac type time-frequency distribution. Math. Methods Appl. Sci. 40(8), 2815–2833 (2017)
Li, J., Fang, Y., Zhang, L.: A TM-based adaptive learning data-model for trajectory tracking and real-time control of a class of nonlinear systems. IEEE Trans. Circuits Syst. I Regul. Pap. 69(2), 859–871 (2021)
Wen-Yuan, F., Li, X.-D., Qian, T.: Data-driven ILC algorithms using AFD in frequency domain for unknown linear discrete-time systems. J. Frankl. Inst. 359(6), 2445–2462 (2022)
Wen-Yuan, F.: Frequency-domain-based iterative learning control utilizing n-best adaptive Fourier decomposition for nonrepetitive unknown iteration-independent and iteration-varying discrete time-delay systems. Int. J. Robust Nonlinear Control 33(4), 2879–2901 (2023)
Qian, T., Wang, Y.-B.: Adaptive Fourier seriesùa variation of greedy algorithm. Adv. Comput. Math. 34(3), 279 (2011)
Qian, T., Wang, Y.: Remarks on adaptive Fourier decomposition. Int. J. Wavelets Multiresolut. Inf. Process. 11(01), 1350007 (2013)
Zhang, B., Wang, D., Ye, Y.: Cutoff-frequency phase-in iterative learning control. IEEE Trans. Control Syst. Technol. 17(3), 681–687 (2008)
de Rozario, R., Oomen, T.: Data-driven iterative inversion-based control: achieving robustness through nonlinear learning. Automatica 107, 342–352 (2019)
Kurek, J.E., Zaremba, M.B.: Iterative learning control synthesis based on 2-D system theory. IEEE Trans. Autom. Control 38(1), 121–125 (1993)
Shen, D., Wang, Y.: Survey on stochastic iterative learning control. J. Process Control 24(12), 64–77 (2014)
Bristow, D.A., Alleyne, A.G.: Monotonic convergence of iterative learning control for uncertain systems using a time-varying filter. IEEE Trans. Autom. Control 53(2), 582–585 (2008)
Cohen, L.: Time-Frequency Analysis, vol. 778. Prentice Hall, NJ (1995)
Orovic, I., Orlandic, M., Stankovic, S., Uskokovic, Z.: A virtual instrument for time-frequency analysis of signals with highly nonstationary instantaneous frequency. IEEE Trans. Instrum. Meas. 60(3), 791–803 (2010)
Li, Q., Lewis, J.A.: Nanoparticle inks for directed assembly of three-dimensional periodic structures. Adv. Mater. 15(19), 1639–1643 (2003)
Bristow, D.A., Alleyne, A.G., Zheng, D.: Control of a microscale deposition robot using a new adaptive time-frequency filtered iterative learning control. In: Proceedings of the 2004 American Control Conference, vol. 6, pp. 5144–5149. IEEE (2004)
Wang, D., Ye, Y.: Design and experiments of anticipatory learning control: frequency-domain approach. IEEE/ASME Trans. Mechatron. 10(3), 305–313 (2005)
Funding
The funding was provided by the National Natural Science Foundation of China under Grants 61204122 and 62103159.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work is supported in part by the National Natural Science Foundation of China under Grants 61204122 and 62103159.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Fu, WY. Nonlinear time-frequency iterative learning control for micro-robotic deposition system using adaptive Fourier decomposition approach. Nonlinear Dyn 111, 20073–20087 (2023). https://doi.org/10.1007/s11071-023-08921-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11071-023-08921-w