Abstract
This study presents an adaptive controller based on neuro-fuzzy networks and stochastic approximation techniques. The algorithm assumes that the mathematical model of the plant is unknown. An adaptive Fuzzy Rule Emulated Network (FREN) structure is implemented as the main controller. While, a modified version of the Simultaneous Perturbation Stochastic Approximation (SPSA) technique is added as the adaptation algorithm, which estimates the gradient of the plant with respect to the control effort. The proposed FREN+SPSA performance for position control is compared to conventional FREN and classical PID controllers. Experimental tests were performed on a cartesian robotic system, regulating the frequency of a DC motor to follow a desired trajectory. Experimental results show better performance of the proposed FREN+SPSA controller than the conventional FREN and PID controller.
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Acknowledgments
This document is the result of the 257253 research project funded by CONACyT and the 28875 research project funded by SEP-PRODEP. The first author thanks CONACyT for her Ph.D. scholarship.
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Facundo, L., Treesatayapun, C., Baltazar, A. (2019). Adaptive Controller Based on IF-THEN Rules and Simultaneous Perturbation Stochastic Approximation Tuning for a Robotic System. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_54
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DOI: https://doi.org/10.1007/978-3-030-33749-0_54
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