Abstract
In this study, a platform of a fluid-power actuator system with a combination of electro-hydraulic and pneumatic for acceleration tracking control is proposed. Furthermore, a control strategy is provided to obtain high-performance results in controlling the piston's motion. Here, the particle swarm optimization (PSO), a computational method, is appropriately utilized for selecting the parameters of the classical proportional integral derivative (PID) control. The tracking errors are eliminated without the challenge of the tuning process, and the control performance is further enhanced. In order to validate the effectiveness of the control strategy, the numerical simulation results are eventually given. The remarkable result of the paper is that the position tracking control is precisely guaranteed when applying only a traditional PID controller with optimized parameters by using the PSO algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ishak, N., Tajjudin, M., Adnan, R., Ismail, H., & Md Sam, Y. (2011). Real-time application of self-tuning PID in electro-hydraulic actuator. In 2011 IEEE International Conference on Control System, Computing and Engineering, pp. 364–368.
Pourebrahim, M., Ghafari, A. S., & Pourebrahim, M. (2016). Designing a LQR controller for an electro-hydraulic-actuated-clutch model. In Proceedings of 2016 2nd International Conference on Control Science and Systems Engineering, pp. 82–87.
Schkoda, R. F. (2015). Sliding mode control of a hydraulically actuated load application unit with application to wind turbine drivetrain testing. IEEE Transactions on Control Systems Technology, 23(6), 2203–2215.
Vu, M. T., et al. (2021). Station-keeping control of a hovering over-actuated autonomous underwater vehicle under ocean current effects and model uncertainties in horizontal plane. IEEE Access, 9, 6855–6867.
Vu, M. T., Le, T. H., Thanh, H. L. N. N., Huynh, T. T., Van, M., Hoang, Q. D., & Do, T. D. (2021). Robust position control of an over-actuated underwater vehicle under model uncertainties and ocean current effects using dynamic sliding mode surface and optimal allocation control. Sensors, 21(3).
Liu, Y.-J., Zeng, Q., Liu, L., & Tong, S. (2020). An adaptive neural network controller for active suspension systems with hydraulic actuator. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(12), 5351–5360.
Huynh, L.N., Pham, Q.V., Pham, X.Q., Nguyen, T.D., Hossain, M.D. and Huh, E.N., 2019. Efficient computation offloading in multi-tier multi-access edge computing systems: A particle swarm optimization approach. Applied Sciences, 10(1), p.203.
Cen, Y., & An, J. (2019). Intelligent control strategy based on PSO-Elman prediction model for ignition temperature in sintering process. In 2019 Chinese Control Conference, pp. 2846–2851.
Hoang, Q.-D., Lee, S.-G., & Dugarjav, B. (2019). Super-twisting observer-based integral sliding mode control for tracking the rapid acceleration of a piston in a hybrid electro-hydraulic and pneumatic system. Asian Journal of Control, 21(1), 483–498.
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95—International Conference on Neural Networks, Vol. 4, pp. 1942–1948.
Acknowledgements
This research was supported by the Institute of Mechanical Engineering, Vietnam Maritime University and by Thu Dau Mot University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Hoang, QD., Nguyen, B.H., Huynh, L.N.T. (2023). Particle Swarm Optimization for Acceleration Tracking Control of an Actuator System. In: Nguyen, T.D.L., Lu, J. (eds) Machine Learning and Mechanics Based Soft Computing Applications. Studies in Computational Intelligence, vol 1068. Springer, Singapore. https://doi.org/10.1007/978-981-19-6450-3_14
Download citation
DOI: https://doi.org/10.1007/978-981-19-6450-3_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-6449-7
Online ISBN: 978-981-19-6450-3
eBook Packages: Computer ScienceComputer Science (R0)