Particle Swarm Optimization-Based PID Control for Position Tracking of Nonlinear Hydraulic Servo Systems
Abstract
Nonlinearity is inherent in hydraulic servomechanisms, as the flow rate-system pressure relationship is complex, and nonlinear friction and the fluid bulk modulus further complicate the system. This study aims to present an optimal Proportional-Integral-Derivative (PID) control method for the angular positioning of a hydraulic servo motor based on the Particle Swarm Optimization (PSO) technique. To improve the controller’s performance, an optimized multi-objective cost function is proposed that combines Integral Squared Error (ISE), maximum overshoot, and steady-state error criteria. An extensive nonlinear mathematical model of the hydraulic servo mechanism is designed and simulated in MATLAB/Simulink. Optimal PID parameter tuning using the PSO algorithm involves a swarm of 300 particles over 30 iterations. The simulation results show that the developed control scheme exhibits fast, accurate tracking of the reference signals without overshoot or oscillation. It takes approximately 4 seconds to track the shaft reference of the motor while maintaining tracking and system stability. This issue demonstrates the efficiency of the developed PSO-based PID controller for compensating nonlinearity without a nonlinear gradient technique. This technique offers a suitable control algorithm that requires fewer computing resources than other control techniques in the industrial application field, where the hydraulic servomechanism operates under nonlinear dynamics.
Keywords:
Particle swarm optimization, Hydraulic Jack, Nonlinear dynamics, Proportional-integral-derivative controller, Position controlReferences
- [1] Bobrow, J. E., & Lum, K. (1996). Adaptive, high bandwidth control of a hydraulic actuator. Journal of dynamic systems, 118(4), 714–720. https://doi.org/10.1115/1.2802347
- [2] Chern, T. L., & Wu, Y. C. (1991). Design of integral variable structure controller and application to electrohydraulic velocity servosystems. IEE proceedings d (control theory and applications) (Vol. 138, pp. 439–444). IET. https://doi.org/10.1049/ip-d.1991.0060
- [3] Fung, R. F., & Yang, R. T. (1998). Application of VSC in position control of a nonlinear electrohydraulic servo system. Computers & structures, 66(4), 365–372. https://doi.org/10.1016/S0045-7949(97)00084-9
- [4] Vossoughi, G., & Donath, M. (1995). Dynamic feedback linearization for electrohydraulically actuated control systems. Journal of dynamic systems measurement and control, 117(4), 468–477. https://doi.org/10.1115/1.2801102
- [5] Aly, A. A. (2004). Modeling and control of an electro-hydraulic servo motor applying velocity feedback control strategy. International mechanical engineering conference, IMEC (pp. 335–342). IMEC2004. https://d1wqtxts1xzle7.cloudfront.net/35580054/Pap7_Hydraulic_vel_feedback_control-libre.pdf?1416054121
- [6] Jones, A. H., Ajlouni, N., & Uzam, M. (1996). Online frequency domain identification and genetic tuning of pid controllers. Proceedings 1996 IEEE conference on emerging technologies and factory automation. etfa ’96 (Vol. 1, pp. 261–266 vol.1). IEEE. https://doi.org/10.1109/ETFA.1996.573302
- [7] Ye, Y., Yin, C. B., Gong, Y., & Zhou, J. (2017). Position control of nonlinear hydraulic system using an improved PSO based PID controller. Mechanical systems and signal processing, 83, 241–259. https://doi.org/10.1016/j.ymssp.2016.06.010
- [8] Wonohadidjojo, D. M., Kothapalli, G., & Hassan, M. Y. (2013). Position control of electro-hydraulic actuator system using fuzzy logic controller optimized by particle swarm optimization. International journal of automation and computing, 10(3), 181–193. https://doi.org/10.1007/s11633-013-0711-3
- [9] Alqadasi, M. M. A., Othman, S. M., Rahmat, M. F., & Abdullah, F. (2019). Optimization of PID for industrial electro-hydraulic actuator using PSOGSA. TELKOMNIKA (telecommunication computing electronics and control), 17(5), 2625–2635. http://doi.org/10.12928/telkomnika.v17i5.12808
- [10] Abdullah, Z. M. (2017). Fuzzy controller parameters optimization based particle swarm optimization algorithm for electro-hydraulic system. Anbar journal of engineering sciences, 8(1), 120–133. https://doi.org/10.37649/aengs.2017.130764
- [11] Liu, W. M., Han, M., Fan, X. Q., Zhao, J., & Zhou, Y. (2025). Particle swarm optimization fuzzy PID control strategy for hydraulic system of log-core veneer lathe. Journal of computers, 36(4), 267–285. http://www.csroc.org.tw/journal/JOC36-4/JOC3604-17.pdf
- [12] Liu, X., Shan, Z., Yang, F., & Li, J. (2023). Research on key problems of synchronous control of hydraulic system based on particle swarm fuzzy PID. 2023 IEEE international conference on mechatronics and automation (ICMA) (pp. 1732–1737). IEEE. 10.1109/ICMA57826.2023.10215646
- [13] Bingul, Z., & Karahan, O. (2021). Real-time trajectory tracking control of Stewart platform using fractional order fuzzy PID controller optimized by particle swarm algorithm. Industrial robot: the international journal of robotics research and application, 49(4), 708–725. https://doi.org/10.1108/IR-07-2021-0157