scholarly journals USE OF NEURAL NETWORKS IN ADAPTIVE ELECTRIC CAR CONTROL

2021 ◽  
Vol 1 (38) ◽  
pp. 213-222
Author(s):  
M. Vesela ◽  
I. Klymenko ◽  
Y. Melnikova
Keyword(s):  
Neural Networks ◽  
Power Plant ◽  
Fuzzy Controller ◽  
Driving Cycle ◽  
Operating Conditions ◽  
Adaptation Algorithm ◽  
Electric Car ◽  
Lack Of Information ◽  
Neuro Fuzzy

To overcome the lack of information about the parameters of the driving cycle of the electric car, neural networks are used, which provide adaptive control that allows you to adapt. electric car to external operating conditions, as well as to compensate for inaccuracies in mathematical models. Use of iterative optimization of parameters allows to adjust optimum work of power plant of the electric car (PEC) in the course of its movement. This method allows you to use a single approach to study different processes, regardless of the parametric features of electric vehicles. To accelerate adaptation, the neurocontroller and neural network model are trained using a reference control model, which is either an optimal strategy or a strategy based on logical rules of choice, obtained by methodical programming for a given driving cycle. Based on the results of the research, an adaptation algorithm is proposed. The expressions given in the article allow to carry out adaptation of the power plant on the basis of hybrid to the current driving cycle on the basis of the concept of training of the neuro-fuzzy controller with reinforcement. The expressions given in the article allow to carry out adaptation of the power plant on the basis of hybrid to the current driving cycle on the basis of the concept of training of the neuro-fuzzy controller with reinforcement. The purpose of training the neuro-fuzzy controller is the formation of such control effects of the power plant, which would reduce the quadratic value of the assessment of the quality of management.

scholarly journals Configuring adaptive PID-controllers of the automatic speed control system of the GTE

2020 ◽  
Vol 8 (6) ◽  
pp. 143-156
Author(s):  
K. E. Chertilin ◽  
V. D. Ivchenko
Keyword(s):  
Control System ◽  
Automatic Control ◽  
Gas Turbine ◽  
Adaptive Systems ◽  
Fuzzy Controller ◽  
Operating Conditions ◽  
Rule Base ◽  
Adaptation Algorithm ◽  
Automatic Control Systems

For non-stationary objects with parameters, which could be changed significantly during operation, using conventional controllers in the form of proportional-integraldifferential regulators may not provide the required quality of the system. Therefore, it is desirable to create an adaptive automatic control system with the structure and parameters of the control regulator that are purposefully changed to ensure the system adaptation, that is based on information about the properties of the object of regulation and external influences, to the changing operating conditions. The problem of designing adaptive systems is one of the most important in control theory and related fields. This is conditioned by two factors: the complexity of solving the problem as a whole and the presence of a large number of technically diverse situations that need to be adapted and optimized. In the paper, an adaptive system for the automatic control of the speed of a gas turbine engine, which includes a magnetic amplifier, a DC motor with a gearbox, a fuel supply valve and a tachogenerator, is developed. For adaptive control execution, three proportional-integral-differential controllers were proposed: "classic", fuzzy and neurofuzzy. The parameters of the "classic" controller were optimized using linear programming methods. The membership functions and the rule base were proposed for the fuzzy controller. An adaptation algorithm was selected for the neuro-fuzzy controller. Three controllers were used for three engine-operating modes: low-gas, cruiser and maximum during the computer simulation of the system. A comparative analysis of the quality of the three regulators was performed and it is based on the obtained transient characteristics. The derived results can be used in the development of automatic control systems for gas turbine engines.

A Neuro-Fuzzy Controller for Compensation of Voltage Disturbance in SMIB System

2017 ◽  
Vol 5 (1) ◽  
pp. 72
Author(s):  
Budi Srinivasarao ◽  
G. Sreenivasan ◽  
Swathi Sharma
Keyword(s):  
Power Quality ◽  
Power Distribution ◽  
Fuzzy Controller ◽  
Quality Issue ◽  
Simulink Model ◽  
Neuro Fuzzy ◽  
Current Voltage ◽  
Control Circuits ◽  
Electrical Loads

Since last decade, due to advancement in technology and increasing in the electrical loads and also due to complexity of the devices the quality of power distribution is decreases. A Power quality issue is nothing but distortions in current, voltage and frequency that affect the end user equipment or disoperation; these are main problems of power quality so compensation for these problems by DPFC is presented in this paper. The control circuits for DPFC are designed by using line currents, series reference voltages and these are controlled by conventional Neuro-Fuzzy controllers. The results are observed by MATLAB/SIMULINK model.

Turbo Gas Power Plants Performance Evaluation for Putting Into Commercial Operation

2009 ◽  
Author(s):  
Erik Rosado Tamariz ◽  
Norberto Pe´rez Rodri´guez ◽  
Rafael Garci´a Illescas
Keyword(s):  
Power Plant ◽  
Power Plants ◽  
Performance Test ◽  
Operating Conditions ◽  
Technical Requirements ◽  
Requirements Specifications ◽  
Commercial Operation ◽  
International Regulations ◽  
Operational Tests

In order to evaluate the performance of new turbo gas power plants for putting in commercial operation, it was necessary to supervise, test and, if so the case, to approve the works of commissioning, operational and acceptance of all equipments and systems that constitute the power plant. All this was done with the aim of guaranteeing the satisfactory operation of these elements to accomplish the function for which they were developed. These activities were conducted at the request of the customer to confirm and observe that the evidence of the tests was carried out according to the specifications and international regulations. The putting into commercial operation activities were done in collaboration with the supplier and manufacturer of equipment, the client and the institution responsible for certification and approval of the plant. All this in a logical and chronological order for the sequence of commissioning tests, operation and acceptance. Commissioning tests were carried out on-site at normal operating conditions, according to the design and operation needs of each power plant of a group of 14. Once the commissioning tests were completely executed and in a satisfactory manner, operational tests of the plants were developed. This was done by considering that they must operate reliable, stable, safe and automatically, satisfying at least, one hundred hours of continuous operation at full load. After evaluating the operational capacity of the machine, it was necessary to determinate the quality of the plant by carrying out a performance test. Finally, it was verified if every unit fulfills the technical requirements established in terms of heat capacity of the machine, noise levels and emissions. As a result of this process, it is guaranteed to the customer that the turbo gas power plants, their systems and equipments, satisfy the requirements, specifications and conditions in agreement with the supplier and manufacturers referring to the putting into commercial operation of the plant.

Neuro-fuzzy controller for gas turbine in biomass-based electric power plant

2002 ◽  
Vol 60 (3) ◽  
pp. 123-135 ◽  
Author(s):  
Francisco Jurado ◽  
Manuel Ortega ◽  
Antonio Cano ◽  
José Carpio
Keyword(s):  
Power Plant ◽  
Electric Power ◽  
Gas Turbine ◽  
Fuzzy Controller ◽  
Electric Power Plant ◽  
Neuro Fuzzy

scholarly journals The Application of a Neuro-Fuzzy Adaptive Crane Control System

2010 ◽  
Vol 14-15 (1) ◽  
pp. 247-258
Author(s):  
Jarosław Smoczek ◽  
Janusz Szpytko
Keyword(s):  
Control System ◽  
Material Handling ◽  
Fuzzy Controller ◽  
Laboratory Model ◽  
Crane Control ◽  
Neuro Fuzzy ◽  
To Come ◽  
Fuzzy Control Algorithm ◽  
Fuzzy Adaptive

The Application of a Neuro-Fuzzy Adaptive Crane Control SystemThe unconventional methods, mostly based on fuzzy logic, are often addressed to a problem of anti-sway crane control. The problem of practical application of those solutions is important owing to come the growing expectations for time and precision of transportation operations and exploitation quality of material handling devices. The paper presents the designing methods of an adaptive anti-sway crane control system based on the neuro-fuzzy controller, as well as the software and hardware equipments used to aid the programming realization the fuzzy control algorithm on a programmable logic controller (PLC). The proposed application of control system was tested on the laboratory model of an overhead traveling crane.

scholarly journals Optimal architecture for artificial neural networks as pressure estimator

RBRH ◽  
2021 ◽  
Vol 26 ◽  
Author(s):  
Rui Gabriel Modesto de Souza ◽  
Bruno Melo Brentan ◽  
Gustavo Meirelles Lima
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Distribution ◽  
Distribution Networks ◽  
Hydraulic Parameter ◽  
Lack Of Information ◽  
Accurate Indication ◽  
Pipe Roughness ◽  
Artificial Neural

ABSTRACT The knowledge of hydraulic parameters in water distribution networks can indicate problems in real time, such as pipe bursts, small leakages, increase in pipe roughness and illegal connections. However, an accurate indication relies on the quantity and quality of the data acquired, i.e., the number of sensors used to monitor the network and their location. It is not economic feasible have a great number of sensors, thus, the use of artificial intelligence, such as Artificial Neural Networks (ANNs) can reduce the lack of information necessary to identify problems, estimating hydraulic parameter through the few information collected. The reliability of ANNs depends on its architecture, so, in this paper, different conditions are tested for ANN training to identify which are the most relevant parameters to be adjusted when the ANN is used for pressure estimation.

scholarly journals Development of synthetic methodology of neuro-fuzzy controller adjusted by genetic algorithm

2018 ◽  
pp. 82-90
Author(s):  
N. A. Pervushina ◽  
D. E. Donovskiy ◽  
A. N. Khakimova
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Controller ◽  
Dynamic Control ◽  
Classical Problem ◽  
Control Object ◽  
Controller Synthesis ◽  
Synthetic Methodology ◽  
Neuro Fuzzy ◽  
Random Disturbances

The paper focuses on a synthetic methodology of a neuro-fuzzy controller adjusted by genetic algorithm for a dynamic control object. An algorithm for controller synthesis and a genetic algorithm for adjusting the controller's parameters have been developed. The methodology has been tested on the classical problem of stabilizing a vertical pendulum on a mobile trolley. The results obtained confirm the efficiency of the methodology and allow for the conclusion that the neuro-fuzzy controller when appropriately adjusted ensures high quality of the stabilization system, even if there are random disturbances on the dynamic object

scholarly journals ANFIS control of a shunt active filter based with a five-level NPC inverter to improve power quality

2021 ◽  
Vol 11 (3) ◽  
pp. 1886
Author(s):  
Mahmoud Mostefa Tounsi ◽  
Ahmed Allali ◽  
Houari Merabet Boulouiha ◽  
Mouloud Denaï
Keyword(s):  
Power Quality ◽  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Harmonic Distortion ◽  
Active Filter ◽  
Nonlinear Loads ◽  
Inference System ◽  
Shunt Active Filter ◽  
Neuro Fuzzy

This paper addresses the problem of power quality, and the degradation of the current waveform in the distribution network which results directly from the proliferation of the nonlinear loads. We propose to use a five-level neutral point clamped (NPC) inverter topology for the implementation of the shunt active filter (SAPF). The aim of the SAPF is to inject harmonic currents in phase opposition at the connection point. The identification of harmonics is based on the pq method. A neuro-fuzzy controller based on ANFIS (adaptive neuro fuzzy inference system) is designed for the SAPF. The simulation study is carried out using MATLAB/Simulink and the results show a significant improvement in the quality of energy and a reduction in total harmonic distortion (THD) in accordance with IEC standard, IEEE-519, IEC 61000, EN 50160.

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