Araştırma Makalesi

Stabilization of Inverted Pendulum System Using Fuzzy Cognitive Map Based PD Controllers

15 Ağustos 2020
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Stabilization of Inverted Pendulum System Using Fuzzy Cognitive Map Based PD Controllers

Abstract

Soft computing techniques are frequently used in modeling and control applications of nonlinear systems. However, the fuzzy cognitive map method, which is one of the soft computing techniques, is rarely used in control applications as a main controller. In this study, a fuzzy cognitive map based PD controller structure is introduced and used for the stabilization of an inverted pendulum system which is a nonlinear, unstable, and under-actuated system. The proposed controller has two inputs which are the error and the change of error. In the proposed PD controller structure, the crisp input values are fuzzified to be handled in a fuzzy cognitive map process. Then, causal relationships between fuzzified inputs and a control output are defined by using weight parameters. Finally, the crisp control output value which will be applied to the system is obtained by using an activation function. The types of membership functions used for the fuzzification process and the activation function determine the nonlinear characteristics of the proposed fuzzy cognitive map based PD controller. The proposed controller has three tuning parameters which are one output gain and two weight parameters. To show the effectiveness and robustness of the proposed fuzzy cognitive map based PD controller, simulation studies are performed on an inverted pendulum system. Additionally, the performance of the proposed controller is compared with a PD controller. All controller parameters are determined by using a genetic algorithm. Comparison results indicate that the proposed fuzzy cognitive map based PD controller shows better control performance than the classical PD controller.

Keywords

Kaynakça

  1. Er, M., Deng, C., & Wang, N. (2018). A Novel Fuzzy Logic Control Method for Multi-Agent Systems with Actuator Faults. 2018 IEEE International Conference On Fuzzy Systems (FUZZ-IEEE), 1-7.
  2. Amuthameena, S., & Monisa, S. (2017). Design of fuzzy logic controller for a non-linear system. 2017 IEEE International Conference On Electrical, Instrumentation And Communication Engineering (ICEICE), 1-7.
  3. Azizi, M., Ejlali, R., Mousavi Ghasemi, S., & Talatahari, S. (2019). Upgraded Whale Optimization Algorithm for fuzzy logic based vibration control of nonlinear steel structure. Engineering Structures, 192, 53-70.
  4. Zhang, X., Ma, X., Zhu, H., & Liu, H. (2017). Neural network controller design for uncertain nonlinear systems based on backstepping control algorithm. 2017 29Th Chinese Control And Decision Conference (CCDC), 1623-1627.
  5. Zhang, Y., Tao, G., & Chen, M. (2016). Adaptive Neural Network Based Control of Noncanonical Nonlinear Systems. IEEE Transactions On Neural Networks And Learning Systems, 27(9), 1864-1877.
  6. Slama, S., Errachdi, A., & Benrejeb, M. (2018). Model reference adaptive control for MIMO nonlinear systems using RBF neural networks. 2018 International Conference On Advanced Systems And Electric Technologies (IC_ASET), 346-351.
  7. Kosko, B. (1986). Fuzzy cognitive maps. International Journal Of Man-Machine Studies, 24(1), 65-75. Papageorgiou, E., & Salmeron, J. (2013). A Review of Fuzzy Cognitive Maps Research During the Last Decade. IEEE Transactions On Fuzzy Systems, 21(1), 66-79.
  8. Arruda, L., Mendonca, M., Neves, F., Chrun, I., & Papageorgiou, E. (2018). Artificial Life Environment Modeled by Dynamic Fuzzy Cognitive Maps. IEEE Transactions On Cognitive And Developmental Systems, 10(1), 88-101.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Mühendislik

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

15 Ağustos 2020

Gönderilme Tarihi

28 Haziran 2020

Kabul Tarihi

10 Ağustos 2020

Yayımlandığı Sayı

Yıl 2020

Kaynak Göster

APA
Denizci, A., & Ulu, C. (2020). Stabilization of Inverted Pendulum System Using Fuzzy Cognitive Map Based PD Controllers. Avrupa Bilim ve Teknoloji Dergisi, 156-164. https://doi.org/10.31590/ejosat.779601