Optimized Aircraft Electric Control System Based on Adaptive Tabu Search Algorithm and Fuzzy Logic Control

Khalid, Saifullah

doi:10.11591/ijeei.v4i3.221

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…Therefore, hybridization technique is necessary to do between the genetic algorithms with other algorithms to get a better solution. The process of finding solutions by using hybridization between the genetic algorithm with Tabu Search will be used in the future research [32]. To prevent premature convergence, the use of a fuzzy crossover operator can also be applied to the genetic algorithm [33].…”

Section: Resultsmentioning

confidence: 99%

Fodder composition optimization using modified genetic algorithm

Wijayaningrum

Mahmudy

2019

IJEEI

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Determination of the fodder composition is a difficult process because it should simultaneously consider several constraints, such as minimizing the total cost of feed ingredients and maximizing the nutrient needs required by livestock. This study uses a modified genetic algorithm to solve the problem in order to obtain better results. The modification is done by applying numerical methods in generating an initial population of the genetic algorithm. Testing results show that the optimal parameters that can be used to produce the optimal solution are as follows: population size (popsize) is 300, generation number is 400, crossover rate (cr) value is 0.2, and mutation rate (mr) value is 0.6. The modified genetic algorithm provides an average fitness value of 0.142357, while the classical genetic algorithm provides an average fitness value of 0.094354. With additional computational time equal to 110 ms, the use of modified genetic algorithm offered has proven to provide a better result, with a higher fitness value compared with classical genetic algorithm.

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Section: Resultsmentioning

confidence: 99%

Fodder composition optimization using modified genetic algorithm

Wijayaningrum

Mahmudy

2019

IJEEI

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“…Fuzzy control algorithm also includes four parts: fuzzification, knowledge base, fuzzy inference, and clarification. According to the experience of knowledge‐based experts, the research attributes of the control object are fuzzified and the corresponding fuzzy variables are obtained (Khalid, 2016; Pourebrahim, Tohidi, & Younesi, 2018). Then, the fuzzy control amount is obtained through fuzzy inference.…”

Section: Fuzzy Logic Control Theorymentioning

confidence: 99%

Fuzzy logic control theory in clinical anesthesia

2021

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In the context of big data, the application of fuzzy logic control theory to clinical anesthesia will produce more research directions and new technologies. This article mainly studies the application of fuzzy logic control theory in clinical anesthesia. First, after introducing the basic content of fuzzy logic control theory, the determination method of commonly used membership functions, the relevant knowledge of clinical anesthesia, and the fuzzy logic code rate control model, this article describes in detail the basic principle diagram of the clinical anesthesia control system and the clinical anesthesia process Mechanism model. The mathematical model of clinical anesthesia control system is constructed based on the data fusion technology of the parameters of anesthesia depth monitoring. The parameters in the model are adjusted by the time domain analysis method to measure the dynamic characteristics, and the stability of the system is analyzed by root locus method. The heart rate does not change significantly when it is lower than 1 MAC, and the heart rate increases when it reaches 1.5–2 MAC. Experimental results show that the application of fuzzy logic control theory to clinical anesthesia can reduce the risk of clinical anesthesia.

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“…The AI algorithms have been extensively accepted for the controller design in many industrial applications [12]. For example, designing of BLDC rotor speed by fuzzy logic [13], genetic algorithm (GA) for controller design [14], self-turning the PID controller using GA [15], controller design using adaptive tabu search (ATS) [16], aircraft electric control by ATS [17] and control synthesis using current search (CS) [18], IJEEI ISSN: 2089-3272  [19]. Hence, this paper presents the use of AI algorithms for tuning the PID parameters (KP, Ki and Kd) for controlling the BLDC motor with phase advance.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Optimal PID Controller Design for BLDC Motor with Phase Advance

Boonpramuk¹,

Tunyasirut²,

Puangdownreong³

2019

IJEEI

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This paper proposes the artificial intelligence (AI)-based optimal PID controller design optimization of brushless direct current (BLDC) motor speed control with phase advance approach. The proposed control system allows the speed adjustment of the BLDC motor by phase advance technique. In this paper, two selected AI algorithms, i.e., the adaptive tabu search (ATS) and the intensified current search (ICS) are conducted as the optimizer for the PID controller design. The proposed control system is simulated by MATLAB/SIMULINK. Results obtained by the ATS and ICS will be compared with those obtained by the Ziegler-Nichols (ZN) tuning rule and the genetic algorithm (GA). It shows that the speed response of the BLDC motor by phase advance with the PID controller optimized by the ICS outperforms better than the ZN, GA and ATS.

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Optimized Aircraft Electric Control System Based on Adaptive Tabu Search Algorithm and Fuzzy Logic Control

Cited by 9 publications

References 8 publications

Fodder composition optimization using modified genetic algorithm

Fodder composition optimization using modified genetic algorithm

Fuzzy logic control theory in clinical anesthesia

Artificial Intelligence-Based Optimal PID Controller Design for BLDC Motor with Phase Advance

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