Skip to main content
SpringerLink
Log in

Multi-degree-of-freedom Internal Model Control for Optoelectronic Stabilized Platform Based on Sliding Mode Friction Compensation

  • Regular Papers
  • Control Theory and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

A multi-degree-of-freedom (multi-DOF) control method is proposed in this paper for the optoelectronic platform affected by internal and external disturbances. First, internal model control (IMC) is used to track the desired signal, and combined with radial basis function neural network (RBFNN)-based sliding mode control (SMC) to compensate for friction torque and weaken model uncertainty. Then, linear active disturbance rejection control (LADRC) is introduced to observe and compensate for sensor noise as well as external unknown disturbances, so that the optoelectronic platform can operate under complex working conditions. The input and disturbance sensitivity functions in a pure feedback control system cannot reach their minimum values in the same frequency band, so there is an inherent contradiction between their tracking and disturbance rejection performance. Combining IMC-SMC-RBFNN with LADRC as a multi-DOF controller can guarantee both tracking and disturbance rejection performance. Lyapunov theory and Barbalat lemma prove the asymptotic stability of the control system. Simulations show that the multi-DOF controller has a good control effect under the mixed disturbances such as parameter perturbation, friction torque and sensor noise, which has reference value for the development of practical optoelectronic platform systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. M. Zhang, H. Liu, H. Zhang, and X. Miao, “A hybrid control strategy for the optoelectronic stabilized platform of a seeker,” Optik, vol. 181, pp. 1000–12, 2019.

    Article  Google Scholar 

  2. K. Qi, L. Dai, S. Wang, Y. Yang, Y. Ding, C. Fang, and C. Lin, “Design methodology of a passive vibration isolation system for an optical system with sensitive line-of-sight,” Photonic Sensors, vol. 11, no. 4, pp. 435–447, 2021.

    Article  Google Scholar 

  3. X. Zhou, Y. Shi, L. Li, R. Yu, and L. Zhao, “A high precision compound control scheme based on non-singular terminal sliding mode and extended state observer for an aerial inertially stabilized platform,” International Journal of Control, Automation, and Systems, vol. 18, no. 6, pp. 1498–1509, 2020.

    Article  Google Scholar 

  4. Y. Lang, X. Tian, Y. Kai, and Z. Jiang, “Optimal design of vibration reduction characteristics of optoelectronic platform structure,” Proc. of International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), pp. 461–464, 2020.

  5. C. Bai and Z. Zhang, “A least mean square based active disturbance rejection control for an inertially stabilized platform,” Optik, vol. 174, pp. 609–22, 2018.

    Article  Google Scholar 

  6. Z. Xing, Q. Zhu, Y. Ding, and Z. Wu, “Design of Missile Servo System Based On Internal Model PID Robust Control,” International Conference on Mechatronics and Automation, pp. 1583–1587, 2006.

  7. X. Sun and G. Zhang, “The control technology for line motion actuator of aero-camera,” Proc. of International Conference on Computer, Mechatronics, Control and Electronic Engineering, vol. 3, pp. 333–336, 2010.

    Google Scholar 

  8. Q. Qiao, K. Nie, J. Deng, W. Ren, X. Chen, and Y. Mao, “Sliding mode control of the optoelectronic stabilized platform based on the exponential approach law,” Proc. of 34rd Youth Academic Annual Conference of Chinese Association of Automation (YAC), pp. 407–410, 2019.

  9. T. Zhao, Y. Tan, and X. Zeng, “Modeling hysteresis using hybrid method of continuous transformation and neural networks,” Sensors and Actuators A: Physical, vol. 119, no. 1, pp. 254–262, 2005.

    Article  Google Scholar 

  10. X. Zhou, H. Zhang, and R. Yu, “Decoupling control for two-axis inertially stabilized platform based on an inverse system and internal model control,” Mechatronics, vol. 24, no. 8, pp. 1203–1213, 2014.

    Article  Google Scholar 

  11. L. Simoni, M. Beschi, A. Visioli, and K. J. Åström, “Inclusion of the dwell time effect in the LuGre friction model,” Mechatronics, vol. 66, 102345, 2020.

    Article  Google Scholar 

  12. Z. Li, D. Fan, and S. Fan, “LuGre-model-based friction compensation in direct-drive inertially stabilized platforms,” IFAC Proceedings Volumes, vol. 46, no. 5, pp. 636–642, 2013.

    Article  Google Scholar 

  13. S. I. Han and K. S.Lee, “Robust friction state observer and recurrent fuzzy neural network design for dynamic friction compensation with backstepping control,” Mechatronics, vol. 20, no. 3, pp. 384–401, 2010.

    Article  Google Scholar 

  14. K. J. Åström and R. M. Murray, Feedback Systems: An Introduction for Scientists and Engineers, Princeton University Press, Princeton, 2021.

    MATH  Google Scholar 

  15. W. H. Chen, J. Yang, L. Guo, and S. Li, “Disturbance-observer-based control and related methods—An overview,” IEEE Transactions on industrial electronics, vol. 63, no. 2, pp. 1083–1095, 2015.

    Article  Google Scholar 

  16. K. Zhou and R. Zhang, “A new controller architecture for high performance, robust, and fault-tolerant control,” IEEE Transactions on Automatic Control, vol. 46, no. 10, pp. 1613–1618, 2001.

    Article  MathSciNet  MATH  Google Scholar 

  17. Y. Zhang, S. H. Wang, B. Chang, and W. H. Wu, “Adaptive constrained backstepping controller with prescribed performance methodology for carrier-based UAV,” Aerospace Science and Technology, vol. 92, pp. 55–65, 2019.

    Article  Google Scholar 

  18. Y. Liu, X. Liu, Y. Jing, and S. Zhou, “Adaptive backstepping H∞ tracking control with prescribed performance for internet congestion,” ISA Transactions, vol. 72, pp. 92–99, 2018.

    Article  Google Scholar 

  19. C. Chen and S. Qiu, “Synchronizing spatiotemporal chaos via a composite disturbance observer-based sliding mode control,” Mathematical Problems in Engineering, pp. 1–7, 2014.

  20. L. Wang, X. Liu, and C. Wang, “Disturbance frequency adaptive control for photo-electric stabilized platform based on improving extended state observation,” Optik, vol. 187, pp. 198–204, 2019.

    Article  Google Scholar 

  21. J. Han, “From PID to active disturbance rejection control,” IEEE Transactions on Industrial Electronics, vol. 56, no. 3, pp. 900–906, 2009.

    Article  Google Scholar 

  22. Y. Wu and D. Yue, “Desired compensation adaptive robust control of electrical-optical gyro-stabilized platform with continuous friction compensation using modified Lu-Gre model,” International Journal of Control, Automation, and Systems, vol. 16, no. 5, pp. 2264–2272, 2018.

    Article  Google Scholar 

  23. P. Gutowski and M. Leus, “Estimation of the tangential transverse vibrations effect on the friction force with the use of LuGre model,” Acta Mechanica, vol. 232, no. 10, pp. 3849–3861, 2021.

    Article  MathSciNet  MATH  Google Scholar 

  24. E. Di. Gialleonardo, M. Santelia, S. Bruni, and A. Zolotas, “A simple active carbody roll scheme for hydraulically actuated railway vehicles using internal model control,” ISA Transactions, vol. 120, pp. 55–69. 2022.

    Article  Google Scholar 

  25. Y. Zhang, D. Kim, Y. Zhao, and J. Lee, “PD control of a manipulator with gravity and inertia compensation using an RBF neural network,” International Journal of Control, Automation, and Systems, vol. 18, no. 12, pp. 3083–3092, 2020.

    Article  Google Scholar 

  26. P. Li and G. Zhu, “Robust internal model control of servo motor based on sliding mode control approach,” ISA Transactions, vol. 93, pp. 199–208, 2019.

    Article  Google Scholar 

  27. D. Kurtoglu, B. Bidikli, E. Tatlicioglu, and E. Zergeroglu, “Periodic disturbance estimation based adaptive robust control of marine vehicles,” Ocean Engineering, vol. 219, 108351, 2021.

    Article  Google Scholar 

  28. Z. Tian, L. Yu, H. Ouyang, and G. Zhang, “Transportation and swing reduction for double-pendulum tower cranes using partial enhanced-coupling nonlinear controller with initial saturation,” ISA Transactions, vol. 112, pp. 122–136, 2021.

    Article  Google Scholar 

  29. C. Liu, G. Luo, Z. Chen, and W. Tu, “Measurement delay compensated LADRC based current controller design for PMSM drives with a simple parameter tuning method,” ISA Transactions, vol. 101, pp. 482–92, 2020.

    Article  Google Scholar 

  30. X. Zhu, J. Chen, and Z. H. Zhu, “Adaptive sliding mode disturbance observer-based control for rendezvous with non-cooperative spacecraft,” Acta Astronautica, vol. 183, pp. 59–74, 2021.

    Article  Google Scholar 

  31. G. Yang, J. Yao, and N. Ullah, “Neuroadaptive control of saturated nonlinear systems with disturbance compensation,” ISA Transactions, vol. 122, pp. 49–62, 2022.

    Article  Google Scholar 

  32. G. Zhang, Z. Yu, Z. Xu, and Z. Jin, “Laser frequency noise measurement in a resonant fiber optic gyro,” Optik, vol. 175, pp. 296–303, 2018.

    Article  Google Scholar 

  33. P. T. Chan, A. B. Rad, and J. Wang, “Indirect adaptive fuzzy sliding mode control: Part II: parameter projection and supervisory control,” Fuzzy Sets and Systems, vol. 122, no. 1, pp. 31–43, 2001.

    Article  MathSciNet  MATH  Google Scholar 

  34. X. Du, W. Wu, W. Zhang, and S. Hu, “Research on UUV recovery Active Disturbance Rejection Control Based on LMNN Compensation,” International Journal of Control, Automation, and Systems, vol. 19, no. 7, pp. 2569–2582, 2021.

    Article  Google Scholar 

  35. Z. Gao, “Scaling and bandwidth-parameterization based controller tuning,” Proc. of the 2003 American Control Conference, pp. 4989–4996, 2003.

  36. R. Madonski, M. Stanković, S. Shao, Z. Gao, J. Yang, and S. Li, “Active disturbance rejection control of torsional plant with unknown frequency harmonic disturbance,” Control Engineering Practice, vol. 100, 104413, 2020.

    Article  Google Scholar 

  37. Z. Gao, “Active disturbance rejection control: A paradigm shift in feedback control system design,” Proc. of American Control Conference, 2006.

  38. J. P. V. S. Cunha, R. R. Costa, F. Lizarralde, and L. Hsu, “Peaking free variable structure control of uncertain linear systems based on a high-gain observer,” Automatica, vol. 45, no. 5, pp. 1156–1164, 2009.

    Article  MathSciNet  MATH  Google Scholar 

  39. W. Shen, X. Liu, and X. Su, “High-precision position tracking control of electro-hydraulic servo systems based on an improved structure and desired compensation,” International Journal of Control, Automation, and Systems, vol. 19, pp. 3622–3630, 2021.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of System Engineering, National University of Defense Technology, Changsha, 410005, China

    Shuaishuai Sui & Yiping Yao

  2. School of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, 266061, China

    Tong Zhao

Authors
  1. Shuaishuai Sui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yiping Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tong Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yiping Yao.

Ethics declarations

The authors declare that there is no competing financial interest or personal relationship that could have appeared to influence the work reported in this paper. No funding was received to assist with the preparation of this manuscript. The authors have no relationship with the editors, reviewers and readers of this journal.

Additional information

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Shuaishuai Sui was born in Qingdao, China, in 1996. She received her M.S. degree in control science and engineering from Qingdao University of Science and Technology in 2022. She is now pursuing a doctor’s degree in College of Systems Engineering, National University of Defense Technology, Changsha, China. Her research interests include high-performance computing and modeling, and simulation of complex systems.

Yiping Yao holds his Ph.D. degree in computer science from National University of Defense Technology in 2004. He is currently a professor in College of Systems Engineering, National University of Defense Technology, Changsha, China. He is the leader of computer simulation in the University of Defense Science and technology. His main research interests include high-performance computing.

Tong Zhao was born in Taian, China. He received his Ph.D. degree in control science and engineering professional from Shanghai Jiaotong University, Shanghai, China, in 2005. He is now a full Professor at College of Automation and Electronic Engineering, Qingdao University of Science and Technology. His research interests include intelligent control and model and control of nonlinear systems.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sui, S., Yao, Y. & Zhao, T. Multi-degree-of-freedom Internal Model Control for Optoelectronic Stabilized Platform Based on Sliding Mode Friction Compensation. Int. J. Control Autom. Syst. 21, 3994–4005 (2023). https://doi.org/10.1007/s12555-021-0864-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-021-0864-8

Keywords

Search

Navigation