Probabilistic-fuzzy actuator model in the soft control circuit of an autonomous unmanned system
DOI:
https://doi.org/10.30837/2522-9818.2025.1.061Keywords:
artificial intelligence; soft-computing-based control; autonomous unmanned system; fuzzy systems; probabilistic models.Abstract
The subject matter of the article is artificial intelligence methods and models used in autonomous unmanned system control. The goal of the work is to create a new actuator model for autonomous unmanned systems that implements control decisions made by Artificial Intelligence under conditions of uncertainty. The following tasks were solved in the article: a model of a Probabilistic Fuzzy Actuator (PFA) is proposed and the possibility of its application as a universal controller of the actuators in autonomous systems is investigated. The PFA model, borrowed from biological muscle actuators, is formalized as a set of automata-like elements with a probabilistic mechanism for assigning their input variables calculated on the basis of fuzzy characteristics of control decisions obtained from an AI system that supports soft control technology. The following methods are used – fuzzy control, decision-making under uncertainty based on the confidence factor, automata theory, probability theory. The following results were obtained – a model of PFA borrowed from living beings has been proposed and substantiated; a PFA algorithm has been developed that implements control decisions obtained by the soft control method. Conclusions: Probabilistic Fuzzy Actuator, unlike existing methods of implementing decisions in soft control models, opens up the possibility of implementing commands that do not have an absolute advantage among all potentially possible ones when making decisions. This capability of autonomous system actuators is useful in conditions when the system encounters an unfamiliar situation since all reaction prototypes existing in its memory are characterized by low confidence. In these cases, to maintain autonomy, it is important to try different behaviors, not just the one that ranks first. Besides this, the "trial and error" method is still required by the self-learning model in autonomous systems that rely on it. Computer experiments confirmed the possibility of implementing this mechanism using the proposed PFA model.
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Piegat A (2001), Fuzzy modelling and control. Physica-Verlag, 728 p.
Kargin, A., Petrenko, T. (2022), "Feeling Artificial Intelligence for AI-Enabled Autonomous Systems. In Proc". IEEE Global Conf. on Artificial Intelligence and Internet of Things (GCAIoT). Alamein New City, Egypt, Dec. 18, 2022, Р. 88–93. DOI: 10.1109/GCAIoT57150.2022.10019235
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Arkin, R. (1998), "Behavior-Based Robotics", Automatica. Р. 69–79. DOI: 10.1016/S0005-1098(02)00169-3
Brooks, R. (1986), "A Robust Layered Control System for a Mobile Robot", IEEE Journal on Robotics and Automation, Vol. 2, No. 1, Р. 14–23. DOI: 10.1109/JRA.1986.1087032
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