Volume 15, Issue 3 (Journal of Control, V.15, N.3 Fall 2021)
JoC 2021, 15(3): 23-33 |
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Asadi Y, Maghfoori M, Bijami E. The design of Data-Driven Adaptive Predictive Controller for a class of unknown no-linear system featuring output saturation. JoC 2021; 15 (3) :23-33
URL: http://joc.kntu.ac.ir/article-1-735-en.html
URL: http://joc.kntu.ac.ir/article-1-735-en.html
1- Bahonar university
2- Jiroft university
2- Jiroft university
Abstract: (7467 Views)
Nowadays, industrial systems deal with a wide range of constraints. Output saturation and lack of system model are two types of these constraints. In this paper, a Data-Driven Adaptive Predictive Control (DDAPC) is propounded for a family of unknown non-linear systems featuring output saturation. The design of the control signal only dependent on the input and output data of the system. In this regard, a new adaptive predictive control scheme is suggested using the new developed dynamic linearization model. The stability analysis of the proposed method is provided by proving the boundedness of the tracking error for both time varying and constant desired reference signal and considering the output saturation data, which is a common physical constraint in industrial systems. Furthermore, the proposed method is more robust against the model uncertainties and nonlinearities, in comparison with the common model-based adaptive methods, since its controller design procedures as well as the stability analysis are independent of plant model. To verify the efficiency and applicability of the suggested approach some applicational and numerical simulation examples are provided.
Type of Article: Research paper |
Subject:
Special
Received: 2020/02/4 | Accepted: 2020/11/1 | ePublished ahead of print: 2020/12/2 | Published: 2022/02/1
Received: 2020/02/4 | Accepted: 2020/11/1 | ePublished ahead of print: 2020/12/2 | Published: 2022/02/1
References
1. Benner, Peter, et al., 2017, "Model reduction and approximation: theory and algorithms", SIAM, Vol. 15. [DOI:10.1137/1.9781611974829]
2. [2] Tympakianaki, A., Koutsopoulos, HN., Jenelius, E., 2018, "Robust SPSA algorithms for dynamic OD matrix estimation", Procedia computer science, Vol. 130, pp. 57-64. [DOI:10.1016/j.procs.2018.04.012]
3. [3] Mišković, L., Karimi, A., Bonvin, D., 2003, "Correlation-based tuning of a restricted complexity controller for an active suspension system", European journal of control, Vol. 9, pp. 77-83. [DOI:10.3166/ejc.9.77-83]
4. [4] Karimi, A., Mišković, L., Bonvin, D., 2004, "Iterative correlation‐based controller tuning", International journal of adaptive control and signal processing, Vol. 18, No. 8, pp. 645-664. [DOI:10.1002/acs.825]
5. [5] Li, X., Chen, S.L., Ma, J., et al., "Data-Driven Model-Free Iterative Tuning Approach for Smooth and Accurate Tracking", 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2018. [DOI:10.1109/AIM.2018.8452239]
6. [6] Mircea-Bogdan, R., Precup, R. E. and Petriu, M., 2015, "Constrained data-driven model-free ILC-based reference input tuning algorithm", Acta Polytechnica Hungarica, Vol. 12. No. 1, pp. 137-160. [DOI:10.12700/APH.12.1.2015.1.9]
7. [7] Chi, R., et al, 2017, "Constrained data-driven optimal iterative learning control" Journal of Process Control, Vol. 55, pp. 10-29. [DOI:10.1016/j.jprocont.2017.03.003]
8. [8] Rui, Z., Song, X. Wen, D., and Sun, C., 2013, "Optimal tracking control for a class of unknown discrete-time systems with actuator saturation via data-based ADP algorithm", Acta Automatica Sinica, Vol. 39, No. 9, pp. 1413-1420. [DOI:10.1016/S1874-1029(13)60070-1]
9. [9] Hui, Y., Chi, R., Huang, B., Hou, Z. and Jin S., 2020 "Observer-Based Sampled-Data Model-Free Adaptive Control for Continuous-Time Nonlinear Nonaffine Systems With Input Rate Constraints" in IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 10, pp. 1-10.
10. [10] Bu, X., Wang, Q., Hou, Z., et al.,2018, "Data driven control for a class of nonlinear systems with output saturation", ISA transactions, Vol. 81, pp. 1-7. [DOI:10.1016/j.isatra.2018.07.009]
11. [11] Hou, Z., Yuanming, Z., 2013, "Controller-dynamic-linearization-based model free adaptive control for discrete-time nonlinear systems", IEEE Transactions on Industrial Informatics, Vol. 9, No. 4, pp. 2301-2309. [DOI:10.1109/TII.2013.2257806]
12. [12] Bu, X., Hou, Z., Yu, Q., et al., 2018, "Quantized Data Driven Iterative Learning Control for a Class of Nonlinear Systems With Sensor Saturation", IEEE Transactions on Systems, Man, and Cybernetics: Systems, pp. 1-11.
13. [13] Bu, Xuhui, et al, 2018, "Model free adaptive control for a class of nonlinear systems using quantized information." Asian Journal of Control, Vol. 20, No. 2, pp. 962-968. [DOI:10.1002/asjc.1610]
14. [14] Xu, D., Shi, Y., Ji, Z., 2018, "Model-Free Adaptive Discrete-Time Integral Sliding-Mode-Constrained-Control for Autonomous 4WMV Parking Systems", IEEE Transactions on Industrial Electronics, Vol. 65, No. 1, pp. 834-843. [DOI:10.1109/TIE.2017.2739680]
15. [15] Qiu, X., et al, 2020, "Resilient Model-Free Adaptive Control for Cyber-physical Systems Against Jamming Attack", Neurocomputing. [DOI:10.1016/j.neucom.2020.04.043]
16. [16] Yu, W., et al, 2020, "Model Free Adaptive Control for a Class of Nonlinear Systems with Fading Measurements", Journal of the Franklin Institute. [DOI:10.1016/j.jfranklin.2020.05.041]
17. [17] Guo, Y., et al, 2019, "Data-Driven Model-Free Adaptive Predictive Control for a Class of MIMO Nonlinear Discrete-Time Systems with Stability Analysis", IEEE Access, Vol. 7, pp. 102852-102866. [DOI:10.1109/ACCESS.2019.2931198]
18. [18] Wang, D., 2018, "A Novel Controller Dynamic Linearization-Based Model Free Adaptive Predictive Control for A Class Of Discrete Nonlinear Systems", International Conference on Mechanical, Electronic, Control and Automation Engineering, (MECAE 2018), Atlantis Press. [DOI:10.2991/mecae-18.2018.80]
19. [19] Hou, Z., Ronghu, C., Huijun, G., 2017, "An overview of dynamic-linearization-based data-driven control and applications", IEEE Transactions on Industrial Electronics, Vol. 64, No. 5, pp. 4076-4090. [DOI:10.1109/TIE.2016.2636126]
20. [20] Hou, Z., and Shuangshuang X., 2019, "On model free adaptive control and its stability analysis", IEEE Transactions on Automatic Control, Vol. 64, No. 11, pp. 4555-4569. [DOI:10.1109/TAC.2019.2894586]
21. [21] Hou, Z., and Shangtai J., 2013, "Model free adaptive control: theory and applications. CRC press. [DOI:10.1201/b15752]
22. [22] Narendra, K. S. and Parthasarathy, K., 1990, "Identification and control for dynamic systems using neural networks", IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 4-27.. [DOI:10.1109/72.80202]
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