GENETIC ALGORITHM-PID CONTROLLER FOR MODEL ORDER REDUCTION PANTOGRAPHCATENARY SYSTEM
Nasir A. Al-Awad
muaayed@uomustansiriyah.edu.iqMustansiriyah University, Faculty of Engineering, Computer Engineering Department, Baghdad (Iraq)
Izz K. Abboud
Mustansiriyah University, Faculty of Engineering, Computer Engineering Department, Baghdad (Iraq)
Muaayed F. Al-Rawi
Mustansiriyah University, Faculty of Engineering, Computer Engineering Department, Baghdad (Iraq)
Abstract
Controlling the contact force between the pantograph and the catenary has come to be a requirement for improving the performances and affectivity of high-speed train systems Indeed, these performances can also significantly be decreased due to the fact of the catenary equal stiffness variation. In addition, the contact force can also additionally differ and ought to end up null, which may additionally purpose the loss of contact. Then, in this paper, we current an active manipulate of the minimize order model of pantograph-catenary system .The proposed manipulate approach implements an optimization technique, like particle swarm (PSO), the usage of a frequent approximation of the catenary equal stiffness. All the synthesis steps of the manipulate law are given and a formal evaluation of the closed loop stability indicates an asymptotic monitoring of a nominal steady contact force. Then, the usage of Genetic Algorithm with Proportional-Integral-derivative (G.A-PID) as proposed controller appeared optimum response where, the contacts force consequences to be virtually equal to its steady reference. Finally it seems the advantageous of suggestion approach in contrast with classical manipulate strategies like, Internal mode control(IMC) method, linear quadratic regulator (LQR).The outcomes via the use of MATLAB simulation, suggests (G.A-PID) offers better transient specifications in contrast with classical manipulate.
Keywords:
pantograph system, model reduction, PSO,G.A-PID, IMC, LQRReferences
Al-Awad, N., & Al-Seady, A. (2020). Fuzzy Controller of Model Reduction Distillation Column with Minimal Rules. Applied Computer Science, 16(2), 80–94. https://doi.org/10.23743/acs-2020-14
Google Scholar
Arnold, M., & Simenon, B. (2000). Pantograph and catenary dynamics: a benchmark problem and its numerical solution. Applied Numerical Mathematics, 34(4), 345–362. https://doi.org/10.1016/S0168-9274(99)00038-0
DOI: https://doi.org/10.1016/S0168-9274(99)00038-0
Google Scholar
Bartolini, G., Pisano, A., Punta, E., & Usai, E. (2003). A survey of applications of second-order sliding mode control to mechanical systems. International Journal of Control, 76(9–10), 875–892. https://doi.org/10.1080/0020717031000099010
DOI: https://doi.org/10.1080/0020717031000099010
Google Scholar
Chater, E., Ghani, D., Giri, F., & Haloua, M. (2015). Output feedback control of pantograph–catenary system with adaptive estimation of catenary parameters. Journal of Modern Transportation, 23, 252–261. https://doi.org/10.1007/s40534-015-0085-z
DOI: https://doi.org/10.1007/s40534-015-0085-z
Google Scholar
Giovanelli, D., & Farella, E. (2016). Force Sensing Resistor and Evaluation of Technology for Wearable Body Pressure Sensing. Journal of Sensors, 3, 9391850. https://doi.org/10.1155/2016/9391850
DOI: https://doi.org/10.1155/2016/9391850
Google Scholar
Haamed, R., & Hameed, E. (2020). Controlling the Mean Arterial Pressure by Modified Model Reference Adaptive Controller Based on Two Optimization Algorithms. Applied Computer Science, 16(2), 53–67. https://doi.org/10.23743/acs-2020-12
Google Scholar
Kennedy, J., & Eberhart, R. (2016). Particle swarm optimization. In IEEE International Conference on Neural Networks, (vol. 4, pp. 1942–1948). IEEE. https://doi.org/10.1109/ICNN.1995.488968
DOI: https://doi.org/10.1109/ICNN.1995.488968
Google Scholar
Kłosowski, G., Klepka, T., & Nowacka, A. (2018). Neural Controller for the Selection of Recycled Components in Polymer-Gypsy Mortars. Applied Computer Science, 14(2), 48–59. https://doi.org/10.23743/acs-2018-12
Google Scholar
Lin, Y., Lin, C., & Yang, C. (2007). Robust active vibration control for rail vehicle pantograph. IEEE transactions on vehicular technology, 56(4), 1994–2004. https://doi.org/10.1109/TVT.2007.897246
DOI: https://doi.org/10.1109/TVT.2007.897246
Google Scholar
Liu, R., Qian, C., Liu, S., & Jin, Y.-F. (2016). State feedback control design for Boolean networks. BMC Systems Biology, 10, 70. https://doi.org/10.1186/s12918-016-0314-z
DOI: https://doi.org/10.1186/s12918-016-0314-z
Google Scholar
Makino, T., Yoshida, K., Seto, S., & Makino, K. (2018). Running test on current collector with contact force controller for high-speed railway. JSME International Journal Series C, 40(4), 671–680. https://doi.org/10.1299/jsmec.40.671
DOI: https://doi.org/10.1299/jsmec.40.671
Google Scholar
Matvejevs, An., & Matvejevs, Al. (2011). Optimal Control of Pantograph-Catenary System Based on Parametric Identification. Scientific Journal of Riga Technical University Computer Science. Information Technology and Management Science, 49.
DOI: https://doi.org/10.2478/v10143-011-0036-z
Google Scholar
O’Connor, D., Eppinger, S., Seering, W., & Wormley, D. (1997). Active control of a high-speed pantograph. Journal of Dynamic Systems, Measurement and Control, 119(1), 1–4. https://doi.org/10.1115/1.2801209
DOI: https://doi.org/10.1115/1.2801209
Google Scholar
Pisano, A., & Usai, E. (2008). Contact force regulation in wire-actuated pantographs via variable structure control and frequency-domain techniques. International Journal of Control, 81(11), 1747–1762. https://doi.org/10.1080/00207170701874473
DOI: https://doi.org/10.1080/00207170701874473
Google Scholar
Pourzeynali, S., Lavasani, H.H., & Modarayi, A.H. (2007). Active control of high rise building structures using fuzzy logic and genetic Algorithms. Engineering Structures, 29(3), 346–357. https://doi.org/10.1016/j.engstruct.2006.04.015
DOI: https://doi.org/10.1016/j.engstruct.2006.04.015
Google Scholar
Shudong, W., Jingbo, G., & Guosheng, G. (2008). Research of the active control for high-speed train pantograph. In IEEE International Conference on Cybernetics and Intelligent Systems (pp. 749–753). IEEE. https://doi.org/10.1109/ICCIS.2008.4670754
DOI: https://doi.org/10.1109/ICCIS.2008.4670754
Google Scholar
Authors
Nasir A. Al-Awadmuaayed@uomustansiriyah.edu.iq
Mustansiriyah University, Faculty of Engineering, Computer Engineering Department, Baghdad Iraq
Authors
Izz K. AbboudMustansiriyah University, Faculty of Engineering, Computer Engineering Department, Baghdad Iraq
Authors
Muaayed F. Al-RawiMustansiriyah University, Faculty of Engineering, Computer Engineering Department, Baghdad Iraq
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