System for Automatic Adjustment of Intelligent Controller Parameters

Ignatyev, Vladimir; Soloviev, Viktor; Beloglazov, Denis; Kureychik, Viktor; Ignatyeva, Alexandra; Vorotova, Anastasiia

doi:10.1007/978-3-030-29750-3_18

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Данная работа является продолжением проведенных авторами исследований по разработке методов эффективного управления на основе гибридного подхода [10], интеллектуальных регуляторов [11,12] и методов их оптимизации [13].…”

Section: метод решения задачи управления техническими объектами в условиях неопределенностиunclassified

Анализ Гибридных Регуляторов В Моделях Управления Техническими Объектами В Изменяющихся Условиях

Игнатьев¹,

V.V.²,

Соловьев³

et al. 2021

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paper analyzes hybrid controllers for control models of technical objects operating in changing conditions. It also considers the models which involve control based on hybrid controllers implemented on the basis of sequential interaction between PI-and IPI-FUZZY-controllers and PID-and IPD-FUZZY-controllers with the generated structure of the Sugeno-type fuzzy inference system and the developed ANFIS model.In hybrid controllers, the fuzzy controller rule base is formed automatically using a specially developed algorithm based on data obtained from a classical controller with subsequent training using a neural network. The ANFIS design principle in the form of a hybrid network for PI and IPI-FUZZY controllers is the use of the output signal error indicators, its integral (differential for PID and IPD-FUZZY controllers) and control action. The following aspects have become the development features. In order to test the hybrid network efficiency to identify the fact of its retraining, the authors used the data obtained as a result of the classical regulator operation; to form a training sample for building a hybrid network they used the data obtained as a result of the fuzzy regulator operation. This makes it possible to exclude expert's participation in the synthesis of the fuzzy controller rule base and to ensure efficient and robust control of an object functioning in unforeseen external situations.The IPI-FUZZY-controller and the IPD-FUZZY-controller have shown better quality indicators comparing to the corresponding classical ones, which makes it possible to recommend using in real control systems. The presented models were developed in the Simulink environment and the ANFIS editor of the Fuzzy Logic Toolbox extension package.

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Section: метод решения задачи управления техническими объектами в условиях неопределенностиunclassified

Анализ Гибридных Регуляторов В Моделях Управления Техническими Объектами В Изменяющихся Условиях

Игнатьев¹,

V.V.²,

Соловьев³

et al. 2021

ППС

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“…In CAD, the broadcast method of MOO problem-solving is a natural process with system simulations. It generally involves evolutionary and genetic algorithms (GAs), annealing modeling algorithms, various swarm-intelligence methods, and others [8,9]. Hybrid algorithms combine GAs with other search methods.…”

Section: Introductionmentioning

confidence: 99%

Hybrid genetic‐paired‐permutation algorithm for improved VLSI placement

et al. 2020

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This paper addresses Very large-scale integration (VLSI) placement optimization, which is important because of the rapid development of VLSI design technologies.The goal of this study is to develop a hybrid algorithm for VLSI placement. The proposed algorithm includes a sequential combination of a genetic algorithm and an evolutionary algorithm. It is commonly known that local search algorithms, such as random forest, hill climbing, and variable neighborhoods, can be effectively applied to NP-hard problem-solving. They provide improved solutions, which are obtained after a global search. The scientific novelty of this research is based on the development of systems, principles, and methods for creating a hybrid (combined) placement algorithm. The principal difference in the proposed algorithm is that it obtains a set of alternative solutions in parallel and then selects the best one. Nonstandard genetic operators, based on problem knowledge, are used in the proposed algorithm. An investigational study shows an objective-function improvement of 13%. The time complexity of the hybrid placement algorithm is O(N 2 ).

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