Modelling and Chaotic Based Parameter Optimization of Sliding Mode Controller
Abstract
This study presents a sliding mode controller design for DC motor speed control using optimization algorithms. The design of sliding mode controllers typically requires expert input during the parameter determination phase. Traditionally, these parameters are set through trial-and-error methods based on the experience of specialists. However, this approach can be both time-consuming and costly. The application of optimization methods automates the parameter-tuning process, reducing human intervention and, in turn, minimizing both design time and costs. The goal of this study is to enhance the performance of optimization methods by hybridizing them with chaotic systems. The random structures of chaotic systems allow optimization algorithms to explore a broader solution space, thereby improving their performance. The analyses conducted in this study reveal that hybrid chaotic algorithms outperform their original ones. The data indicate that the use of hybrid algorithms generally leads to a decrease in Steady-State Error. Additionally, it is observed that when all hybrid algorithms are employed, the sliding mode controller does not exhibit any overshoot. The results demonstrate that the sliding mode controller performs effectively, achieving low settling time, rise time, and steady-state error, while also preventing chattering. Among the methods examined, the sliding mode controller optimized with the Chaotic Henry Gas Solubility Optimization algorithm delivers the best performance, ensuring optimal system stability.
Keywords
Chaotic, Henry gas solubility optimization, Hybrid optimization, Sliding mode control
References
- [1] W. Cao, X. Liu, J. Ni, Parameter optimization of support vector regression using Henry gas solubility optimization algorithm, IEEE Access, 8 (2020), 88633-88642. https://doi.org/10.1109/ACCESS.2020.2993267
- [2] M. Liu, T. Liu, M. Zhu, et al., A PI control method with HGSO parameter regulator for trajectory planning of 9-DOF redundant manipulator, Sensors, 22(18) (2022), Article ID 6860. https://doi.org/10.3390/s22186860
- [3] H. Abdel-Mawgoud, S. Kamel, M. Khasanov, et al., Khurshaid, A strategy for PV and BESS allocation considering uncertainty based on a modified Henry gas solubility optimizer, Electr. Power. Syst. Res., 191 (2021), Article ID 106886. https://doi.org/10.1016/j.epsr.2020.106886
- [4] D.T. Mosa, A. Mahmoud, J. Zaki, et al., Henry gas solubility optimization double machine learning classifier for neurosurgical patients, PLoS One, 18(5) (2023), Article ID e0285455. https://doi.org/10.1371/journal.pone.0285455
- [5] W. Xie, C. Xing, J. Wang, et al., Hybrid henry gas solubility optimization algorithm based on the harris hawk optimization, IEEE Access, 8 (2020), 144665-144692. https://doi.org/10.1109/ACCESS.2020.3014309
- [6] M. Abd Elaziz, D. Yousri, Automatic selection of heavy-tailed distributions-based synergy Henry gas solubility and Harris hawk optimizer for feature selection: Case study drug design and discovery, Artif. Intell. Rev., 54(6) (2021), 4685-4730. https://doi.org/10.1007/s10462-021-10009-z
- [7] S. Ekinci, B. Hekimoğlu, D. Izci, Opposition based Henry gas solubility optimization as a novel algorithm for PID control of DC motor, Eng. Sci. Technol., Int. J., 24(2) (2021), 331-342. https://doi.org/10.1016/j.jestch.2020.08.011
- [8] S. Ravikumar, D. Kavitha, CNN-OHGS: CNN-oppositional-based Henry gas solubility optimization model for autonomous vehicle control system, J. Field Robot., 38(7) (2021), 967-979.https://doi.org/10.1002/rob.22020
- [9] B.M. Alshammari, A. Farah, K. Alqunun, et al., Robust design of dual-input power system stabilizer using chaotic Jaya algorithm, Energies, 14(17) (2021), Article ID 5294. https://doi.org/10.3390/en14175294
- [10] E. Emary, H.M. Zawbaa, Impact of chaos functions on modern swarm optimizers, PLoS ONE, 11(7) (2016), Article ID e0158738. https://doi.org/10.1371/journal.pone.0158738
