Decoupling Sliding Mode Control of Underactuated Systems using a Takagi-Kang-Sugeno Fuzzy Brain Emotional Controller and Particle Swarm Optimization

• Autorzy: Pham Duc Hung , Nguyen Viet-Ngu , Le Thi Minh-Tan

Identyfikatory

DOI

http://dx.doi.org/10.15439/2022R33

• Języki publikacji: EN

Abstrakty

EN

A Tagaki-Kang-Sugeno fuzzy brain emotional controller (TFBEC) for decoupling control of underactuated nonlinear systems is developed in this paper. The decoupling sliding mode technique is used to achieve decoupling control performance. An amygdala cortex and a prefrontal cortex comprise the brain emotional model. The prefrontal cortex is an emotional neural network, while the amygdala cortex is a sensory neural network. The proposed TFBEC is adaptive, and the parameters can be adjusted to achieve efficient control performance. A TFBEC is used as the main controller to approximate an ideal controller and achieve the desired control performance, and a robust compensator is used to eliminate the remaining approximation error and achieve system stability. A particle swarm optimization is used to find the optimal learning rates of the proposed method. Finally, the TFBEC control system is demonstrated by controlling a bridge crane system with one degree of under actuation. Simulation results have confirmed the validity of the proposed approach.

Słowa kluczowe

EN

Takagi-Kang-Sugeno fuzzy system; brain emotional controller; bridge-crane system

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 33
• Strony: 195--200
• Opis fizyczny: Bibliogr. 12 poz., rys., tab., wykr.

Twórcy

autor

Pham Duc Hung

• Faculty Electrical and Electronic Engineering, Hung Yen University of Technology and Education, Hai Duong, Vietnam.

autor

Nguyen Viet-Ngu

• Faculty Electrical and Electronic Engineering, Hung Yen University of Technology and Education, Hai Duong, Vietnam.

autor

Le Thi Minh-Tan

• Faculty Electrical and Electronic Engineering, Hung Yen University of Technology and Education, Hai Duong, Vietnam.

Bibliografia

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• [2] K. J. Å ström and B. Wittenmark, Adaptive Control, Reading, Addision-Wesly, Massachusetts, 1995.
• [3] J. E. LeDoux, The Amygdala: Neurobiological Aspects of Emotion, Wiley-Liss, New York, pp. 339-351, 1992.
• [4] C. Balkenius and J. Moren, "Emotional learning: A computational model of the amygdala," Cybernetics and Systems, vol. 32, no. 6, pp. 611-636, 2001.
• [5] C. Lucas, D. Shahmirzadi, and N. Sheikholeslami, "Introducing BELBIC: Brain emotional learning based intelligent controller," International Journal of Intelligent Automation and Soft Computing, vol. 10, no. 1, pp. 11-21, 2004.
• [6] L.–C. Hung and H.-Y. Chung, “Decoupled sliding-mode with fuzzy-neural network controller for nonlinear systems,” International Journal of Approximate Reasoning,vol. 46, no. 1, pp 74-97, 2007.
• [7] Lin, C. M., Pham, D. H., & Huynh, T. T. “Encryption and Decryption of Audio Signal and Image Secure Communications Using Chaotic System Synchronization Control by TSK Fuzzy Brain Emotional Learning Controllers,” IEEE Transactions on Cybernetics, 2021, . http://dx.doi.org/10.1109/TCYB.2021.3134245.
• [8] T.-T. Huynh, C. –M. Lin, N.-Q. –K. Le, N. P. -Nguyen, and F. Chao. "Intelligent wavelet fuzzy brain emotional controller using dual function-link network for uncertain nonlinear control systems." Applied Intelligence, vol. 52, no. 3, pp. 2720-2744, 2022.
• [9] N. Sun, Y.-C. Fang, and X.-Q. Wu, "An enhanced coupling nonlinear control method for bridge cranes," IET Control Theory and Applications, vol. 8, no.13, pp. 1215-1223, 2014.
• [10] C. M. Lin, and Y. J. Mon, "Decoupling control by hierarchical fuzzy sliding-mode controller," IEEE Transactions on Control Systems Technology, vol. 13, no. 4, pp. 593-598, 2005.
• [11] C.-M. Lin, D.-H. Pham, and T.-T. Huynh, “Synchronization of chaotic system using a brain-imitated neural network controller and its applications for secure communications,” IEEE Access, vol. 9, pp. 75923-75944, 2021.
• [12] R. Eberhart and J. Kennedy, "A new optimizer using particle swarm theory," in MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995, pp. 39-43: IEEE.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).