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Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering

Annals of Computer Science and Information Systems, Volume 33

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

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

Citation: Proceedings of the 2022 Seventh International Conference on Research in Intelligent and Computing in Engineering, Vu Dinh Khoa, Shivani Agarwal, Gloria Jeanette Rincon Aponte, Nguyen Thi Hong Nga, Vijender Kumar Solanki, Ewa Ziemba (eds). ACSIS, Vol. 33, pages 195200 ()

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Abstract. 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.


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