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Adaptive controller based on quantum computation and coherent superposition fuzzy rules network with unknown nonlinearities

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Abstract

In the realm of control engineering applications, compensating for unknown dynamics and nonlinearities is of paramount importance for shaping closed-loop performance. This paper introduces a novel solution to this challenge: the adaptive controller based on Quantum-Inspired Fuzzy Rules Emulated Network (QFREN). Leveraging its intrinsic learning capacity, QFREN assimilates human knowledge through a series of IF-THEN rules based on quantum computation principles. By defining quantum states for membership functions, the concept of coherent superposition of tracking errors is employed to effectively mitigate the effects of disturbances and nonlinearities. Learning laws are derived to finely calibrate all network and quantum computation parameters, accompanied by a thorough analysis of closed-loop performance to ensure robustness. Experimental validation and comparative assessments substantiate the efficacy of the proposed scheme, showcasing a reduction in tracking error of at least \(20\%\) compared to recent comparative controllers based on data-driven and quantum-neural network schemes.

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Data availability and access

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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  1. Department of Robotics and Advanced Manufacturing, Center for Research and Advanced Studies (CINVESTAV-IPN), 1062 Industria Metalurgica Av., Ramos Arizpe, Coahuila, 25903, Mexico

    Chidentree Treesatayapun

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Correspondence to Chidentree Treesatayapun.

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Treesatayapun, C. Adaptive controller based on quantum computation and coherent superposition fuzzy rules network with unknown nonlinearities. Appl Intell (2024). https://doi.org/10.1007/s10489-024-05446-6

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