Compensation of Valve Stiction Using Nonlinear Controller Optimized with PSO Algorithm

Subject Areas : vibration and control

Hamed Khodadadi ¹ , Mohammad Mahdi Giahi ² , Hamid Ghadiri ³

1 -
2 -
3 -

Received: 2024-11-24 Accepted : 2025-05-13 Published : 2025-11-23

Keywords: Backstepping controller, Particle swarm algorithm, Valve stiction, Uncertain condition,

Abstract :

Industrial processes such as oil, gas, petrochemicals, steel, and cement heavily rely on precise fluid transfer between vessels. Valves are critical components in these systems. While technological advancements have enabled automated valve control, challenges persist in valve actuators due to some nonlinear factors, such as stiction. This paper investigates the impact of stiction on valve actuator performance. A stiction model, incorporating disturbances, will be developed and transformed into a state-space representation. Subsequently, an optimized backstepping controller, enhanced by particle swarm optimization (PSO), will be designed to address this nonlinearity. The valve's influence on the actuator will be modeled as a constant torque. By considering the system's internal dynamics, a stabilizing controller will be developed to ensure system stability across all dynamic states. Lyapunov theory will be used to verify stability. MATLAB simulations will evaluate the controller's effectiveness in stabilizing the nonlinear stiction valve model and achieving the desired control performance. 

References:

[1] Villeda-Hernandez, M., Baker, B. C., Romero, C., Rossiter, J. M., Dicker, M. P. M., & Faul, C. F. J. (2023). Chemically Driven Oscillating Soft Pneumatic Actuation. Soft robotics, 10(6), 1159–1170. https://doi.org/10.1089/soro.2022.0168.
[2] Arbabi Yazdi, Y., Toossian Shandiz, H., & Gholizadeh Narm, H. (2022). Stiction detection in control valves using a support vector machine with a generalized statistical variable. ISA transactions, 126, 407–414. https://doi.org/10.1016/j.isatra.2021.07.020:
[3] Mishra, P., Kumar, V., & Rana, K. P. S. (2020). A nonlinear framework for stiction compensation in ratio control loop. ISA transactions, 103, 319–342. https://doi.org/10.1016/j.isatra.2020.04.009.
[4] Yang, Z., Yang, Q., & Sun, Y. (2015). Adaptive Neural Control of Nonaffine Systems With Unknown Control Coefficient and Nonsmooth Actuator Nonlinearities. IEEE transactions on neural networks and learning systems, 26(8), 1822–1827. https://doi.org/10.1109/TNNLS.2014.2354533.
[5] M. M. Giahi, M. Naderi, S. O. Shahdi and A. Ehsani, "A review of the Robotic Artificial Hand: (Advances, Applications and Challenges)," 2024 10th International Conference on Artificial Intelligence and Robotics (QICAR), Qazvin, Iran, Islamic Republic of, 2024, pp. 157-163, doi: 10.1109/QICAR61538.2024.10496612.
[6] Ghadiri, H., Khodadadi, H., Eijei, H. & Ahmadi, M. Pso based takagi-sugeno fuzzy pid controller design for speed control of permanent magnet synchronous motor. Facta Univ. Ser. Electron. Energ. 34(2), 203–217 (2021)
[7] H. B. Novin and H. Ghadiri, "Particle swarm optimization base explicit model predictive controller for limiting shaft torque," 2017 5th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS), Qazvin, Iran, 2017, pp. 35-40, doi: 10.1109/CFIS.2017.8003593.
[8] Khodadadi, H., Sedigh, A.K., Ataei, M. et al. (2017), “Nonlinear Analysis of the Contour Boundary Irregularity of Skin Lesion Using Lyapunov Exponent and K-S Entropy,” Journal of Medical Biology Engineering, 37, 409–419.
[9] Ghadiri, H., Mohammadi, A. & Khodadadi, H, (2022), “Fast terminal sliding mode control based on SDRE observer for two-axis gimbal with external disturbances,” J Braz. Soc. Mech. Sci. Eng. 44, 70.
[10] Ghadiri H, Khodadadi H, Mobayen S, Asad JH, Rojsiraphisal T, Chang, (2021), “A. Observer-Based Robust Control Method for Switched Neutral Systems in the Presence of Interval Time-Varying Delays,” Mathematics, 9 (19), 2473.
[11] Ghadiri, H., Khodadadi, H., & Hazareh, G. A. (2024). “Finite-time integral fast terminal sliding mode control for uncertain quadrotor UAV based on state-dependent Riccati equation observer subjected to disturbances,” Journal of Vibration and Control, 30 (11-12), 2528-2548.
[12] Khodadadi, H., Khaki-Sedigh, A., Ataei, M., & Jahed-Motlagh, M. R. (2018), “Applying a modified version of Lyapunov exponent for cancer diagnosis in biomedical images: the case of breast mammograms,” Multidimensional Systems and Signal Processing, 29, 19-33.
[13] Rostalski, P., Besselmann, T., Barić, M., Belzen, F. V., & Morari, M. (2007). “A hybrid approach to modelling, control and state estimation of mechanical systems with backlash,” International Journal of Control, 80 (11), 1729–1740.
[14] Lv, C., Liu, Y., Hu, X., Guo, H., et. Al. (2018), “Simultaneous Observation of Hybrid States for Cyber-Physical Systems: A Case Study of Electric Vehicle Powertrain,” IEEE transactions on cybernetics, 48 (8), 2357–2367.
[15] Liu, W., Duan, G., Hou, M., & Kong, H. (2024), “Robust Adaptive Control of High-Order Fully-Actuated Systems: Command Filtered Backstepping with Concurrent Learning,” IEEE Transactions on Circuits and Systems I.
[16] R. Samani and H. Khodadadi, (2017), "A particle swarm optimization approach for sliding mode control of electromechanical valve actuator in camless internal combustion engines," 2017 IEEE International Conference on Environment (EEEIC / I&CPS Europe), 1-4.
[17] Ahmadpour, M. R., Ghadiri, H., & Hajian, S. R. (2021). “Model predictive control optimisation using the metaheuristic optimisation for blood pressure control,” IET systems biology, 15 (2), 41–52.