Application of Dynamic Systems Family for Synthesis of Fuzzy Control with Account of Non-linearities

Lozynskyy, Andriy; Demkiv, Lyubomyr

doi:10.15598/aeee.v14i5.1717

Cited by 5 publications

(2 citation statements)

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“…In this work, the fuzzy logic control is chosen because of its adaptive tuning capabilities, ability to deal with uncertainty and non-linearity while also its rapid operation [9][10][11][12]. Besides, a reinforcement learning algorithm, is utilized for tuning the parameters of membership function of the fuzzy controller, which minimizes the complexity of fuzzy controller tuning and synthesis.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy controller, designed by reinforcement learning, for vehicle traction system application

Demkiv¹,

Lozynskyy²,

Vantsevich³

et al. 2021

Math. Model. Comput.

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In this article, a fuzzy controller tuned by reinforcement learning is proposed. The developed algorithm utilizes a fuzzy logic theory and a reinforcement learning for fine-tuning parameters of the membership function for the fuzzy controller. Apart from the fuzzy controller developed, a fuzzy corrector of reference input (set-point) signal to the controller is applied. The fuzzy corrector changes the input (reference) signal of the system and takes into account an original reference input and type of external disturbances. Thus, the designed fuzzy control that is tuned by reinforcement learning is capable to ensure the stable, optimal, and safe performance of the system and takes into account external disturbances. To verify the performance of the proposed controller, the adaptive fuzzy controller tuned by reinforcement learning is applied to the mathematical model of a wheel locomotion module of an electric vehicle to advance a traction control system. Therefore, the effectiveness of the proposed adaptive fuzzy controller is proven through the simulation results.

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Section: Introductionmentioning

confidence: 99%

Fuzzy controller, designed by reinforcement learning, for vehicle traction system application

Demkiv¹,

Lozynskyy²,

Vantsevich³

et al. 2021

Math. Model. Comput.

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“…However, due to simplifications done the given linearization technique does not fully consider the peculiarities of the non linear components in the investigated system. An approach that utilizes the unstable behavior of the subsystem, as well as smooth switching between controllers of the linear subsys tems, was further investigated by the authors in [32]. It has been proved that the application of the controller synthesized for one of the subsystems with unstable behavior improves the characteristics of the dynamic system keeping its stability.…”

Section: Introductionmentioning

confidence: 99%

Review of methods for energy-efficiency improvement in induction machines

Diachenko¹,

Азюковський²

2020

Nauk. visn. nat. hirn. univ.

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Purpose. To present a comprehensive analysis of domestic and foreign experience regarding the existing optimization tech niques in the problems of the power losses minimization in electromechanical systems with an induction machine for reduction of the total electricity consumed from the grid.Methodology. A detailed study of the developments in the field of efficiency optimization of threephase induction machines through optimal control and design techniques has been done. Special attention is given to vectorcontrolled systems. Sustainable development of a few trends was traced in this domain. The reference value of the fieldgenerating current is an additional degree of freedom in the mathematical model of the investigated system. It influences the magnetic flux linkage dynamics and mechanical torque equations. Hence, the implemented model allows for a comparative analysis of different approaches to ensure minimum energy consumption with an adequate intensity of transients.findings. Among numerous control techniques, simple state control, loss modelbased control and search control efficiency optimization algorithms have been highlighted. The simulation example on efficiency optimization of an asynchronous machine was performed in the framework of an indirect fieldoriented control system considering the stepped trajectory of load torque, which is possible as a result of mechanical perturbation or when the motor performs complex speed profiles or counteracts shock loads.originality. The rigorous review indicates that existing optimization algorithms in conventional still can be used for induction motor drive applications. However, some existing problems in achieving the best control were not summarized. Accordingly, for the first time, this review provides suggestions for the future research and development of dynamic energyefficient control in in duction motors.Practical value. The threephase induction motor drives are a nonlinear system that is tough to describe precisely theoretically due to their sudden changes in conditions of operation mode and parameter variation. Thus, advanced algorithms are needed to enhance their performance in addition to effective hardware solutions. The suggested alternative solution will hopefully lead to increased efforts toward the development of advanced control systems for future applications.

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Enhancement of Dynamical Characteristics of a Fuzzy Control System by Using Unstable Subsystem

Lozynskyy

Vantsevitch

Demkiv

2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Application of Dynamic Systems Family for Synthesis of Fuzzy Control with Account of Non-linearities

Cited by 5 publications

References 0 publications

Fuzzy controller, designed by reinforcement learning, for vehicle traction system application

Fuzzy controller, designed by reinforcement learning, for vehicle traction system application

Review of methods for energy-efficiency improvement in induction machines

Enhancement of Dynamical Characteristics of a Fuzzy Control System by Using Unstable Subsystem

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