Log in

Relevant bibliographies by topics / Artificial neural network controller / Journal articles

To see the other types of publications on this topic, follow the link: Artificial neural network controller.

Journal articles on the topic 'Artificial neural network controller'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Artificial neural network controller.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Karunasena, G. M. K. B., H. D. N. S. Priyankara, and B. G. D. A. Madhusank. "Artificial Neural Network vs PID Controller for Magnetic Levitation System." International Journal of Innovative Science and Research Technology 5, no. 7 (2020): 505–11. http://dx.doi.org/10.38124/ijisrt20jul432.

Full text

Abstract:

This research investigates the acceptability of the Artificial Neural Networks (ANN) over the PID Controller for the control of the Magnetic Levitation System (MLS). In the field of advanced control systems, this system identifies as a feedback-controlled, single input- single output (SISO) system. This SISO system used a PID controller for vertical trajectory controlling of a metal sphere in airspace by using an electromagnetic force that directed to upward. The vertical position of the metal sphere controlled according to the applied magnetic force generated by a powerful electromagnet and t

APA, Harvard, Vancouver, ISO, and other styles

2

Joshi, Girisha, and Pinto Pius A J. "ANFIS controller for vector control of three phase induction motor." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (2020): 1177. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1177-1185.

Full text

Abstract:

For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis

APA, Harvard, Vancouver, ISO, and other styles

3

Girisha, Joshi, and Pius A. J. Pinto. "ANFIS controller for vector control of three phase induction motor." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 19, no. 3 (2020): 1177–85. https://doi.org/10.11591/ijeecs.v19.i3.pp1177-1185.

Full text

Abstract:

For variable speed drive applications such as electric vehicles, 3 phase induction motor is used and is controlled by fuzzy logic controllers. For the steady functioning of the vehicle drive, it is essential to generate required torque and speed during starting, coasting, free running, braking and reverse operating regions. The drive performance under these transient conditions are studied and presented. In the present paper, vector control technique is implemented using three fuzzy logic controllers. Separate Fuzzy logic controllers are used to control the direct axis current, quadrature axis

APA, Harvard, Vancouver, ISO, and other styles

4

GÖKÇE, Celal Onur. "Intelligent Quadcopter Control Using Artificial Neural Networks." Afyon Kocatepe University Journal of Sciences and Engineering 23, no. 1 (2023): 138–42. http://dx.doi.org/10.35414/akufemubid.1229424.

Full text

Abstract:

An advanced controller architecture and design for quadcopter control implementation is proposed in this study. Instead of using only the error information as input to the controller, reference and measured outputs are used separately independent from each other. This enhances the performance of the controller of quadcopter being a highly non-linear platform. In this study single layer neural network is directly used as a controller. A complex controller is grown from an initially simple PID controller. This elevates the need for time consuming search in huge parameter space due to very high d

APA, Harvard, Vancouver, ISO, and other styles

5

Katada, Yoshiaki, Takumi Hirokawa, Motoaki Hiraga, and Kazuhiro Ohkura. "MBEANN for Robotic Swarm Controller Design and the Behavior Analysis for Cooperative Transport." Journal of Robotics and Mechatronics 35, no. 4 (2023): 997–1006. http://dx.doi.org/10.20965/jrm.2023.p0997.

Full text

Abstract:

This study focuses on mutation-based evolving artificial neural network (MBEANN), a topology and weight evolving artificial neural network (TWEANN) algorithm. TWEANN optimizes both the connection weights and neural network structure. Primarily, MBEANN uses only mutations to evolve artificial neural networks. An individual in an MBEANN is designed to have a set of sub-networks called operons. Operons are expected to have functions during evolution because they do not recombine with other operons. In this study, we applied MBEANN to design a controller for a robotic swarm on cooperative transpor

APA, Harvard, Vancouver, ISO, and other styles

6

K.J., Rathi, and S. Ali M. "Neural Network Controller for Power Electronics Circuits." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 2 (2017): 49–55. https://doi.org/10.5281/zenodo.4108248.

Full text

Abstract:

Artificial Intelligence (AI) techniques, particularly the neural networks, are recently having significant impact on power electronics. This paper explores the perspective of neural network applications in the intelligent control for power electronics circuits. The Neural Network Controller (NNC) is designed to track the output voltage and to improve the performance of power electronics circuits. The controller is designed and simulated using MATLAB-SIMULINK

APA, Harvard, Vancouver, ISO, and other styles

7

Scott, Gary M., Jude W. Shavlik, and W. Harmon Ray. "Refining PID Controllers Using Neural Networks." Neural Computation 4, no. 5 (1992): 746–57. http://dx.doi.org/10.1162/neco.1992.4.5.746.

Full text

Abstract:

The KBANN (Knowledge-Based Artificial Neural Networks) approach uses neural networks to refine knowledge that can be written in the form of simple propositional rules. We extend this idea further by presenting the MANNCON (Multivariable Artificial Neural Network Control) algorithm by which the mathematical equations governing a PID (Proportional-Integral-Derivative) controller determine the topology and initial weights of a network, which is further trained using backpropagation. We apply this method to the task of controlling the outflow and temperature of a water tank, producing statisticall

APA, Harvard, Vancouver, ISO, and other styles

8

Obed, Adel, and Ameer Saleh. "Speed Control of BLDC Motor Based on Recurrent Wavelet Neural Network." Iraqi Journal for Electrical and Electronic Engineering 10, no. 2 (2014): 118–29. http://dx.doi.org/10.37917/ijeee.10.2.7.

Full text

Abstract:

In recent years, artificial intelligence techniques such as wavelet neural network have been applied to control the speed of the BLDC motor drive. The BLDC motor is a multivariable and nonlinear system due to variations in stator resistance and moment of inertia. Therefore, it is not easy to obtain a good performance by applying conventional PID controller. The Recurrent Wavelet Neural Network (RWNN) is proposed, in this paper, with PID controller in parallel to produce a modified controller called RWNN-PID controller, which combines the capability of the artificial neural networks for learnin

APA, Harvard, Vancouver, ISO, and other styles

9

Somwong, Poom, Karn Patanukhom, and Yuthapong Somchit. "Energy-Aware Controller Load Distribution in Software-Defined Networking using Unsupervised Artificial Neural Networks." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 16, no. 1 (2025): 289–314. https://doi.org/10.58346/jowua.2025.i1.018.

Full text

Abstract:

Software-Defined Networking (SDN) enhances network management by separating the control and data planes into controllers and switches, allowing for centralized, programmable networks with multiple controllers. Switches are mapped to controllers and exchange control messages to manage the network, which leads to significant energy consumption. Managing energy in networks has become a critical issue, as dynamic changes in switch loads can cause controller overloads, necessitating the migration of switches to other controllers. As networks grow, energy consumed in control communications becomes a

APA, Harvard, Vancouver, ISO, and other styles

10

Woodford, Grant W., and Mathys C. du Plessis. "Complex Morphology Neural Network Simulation in Evolutionary Robotics." Robotica 38, no. 5 (2019): 886–902. http://dx.doi.org/10.1017/s0263574719001140.

Full text

Abstract:

SUMMARYThis paper investigates artificial neural network (ANN)-based simulators as an alternative to physics-based approaches for evolving controllers in simulation for a complex snake-like robot. Prior research has been limited to robots or controllers that are relatively simple. Benchmarks are performed in order to identify effective simulator topologies. Additionally, various controller evolution strategies are proposed, investigated and compared. Using ANN-based simulators for controller fitness estimation during controller evolution is demonstrated to be a viable approach for the high-dim

APA, Harvard, Vancouver, ISO, and other styles

11

Jiang, Yiming, Chenguang Yang, Shi-lu Dai, and Beibei Ren. "Deterministic learning enhanced neutral network control of unmanned helicopter." International Journal of Advanced Robotic Systems 13, no. 6 (2016): 172988141667111. http://dx.doi.org/10.1177/1729881416671118.

Full text

Abstract:

In this article, a neural network–based tracking controller is developed for an unmanned helicopter system with guaranteed global stability in the presence of uncertain system dynamics. Due to the coupling and modeling uncertainties of the helicopter systems, neutral networks approximation techniques are employed to compensate the unknown dynamics of each subsystem. In order to extend the semiglobal stability achieved by conventional neural control to global stability, a switching mechanism is also integrated into the control design, such that the resulted neural controller is always valid wit

APA, Harvard, Vancouver, ISO, and other styles

12

Asgari, Hamid, Mohsen Fathi Jegarkandi, XiaoQi Chen, and Raazesh Sainudiin. "Design of conventional and neural network based controllers for a single-shaft gas turbine." Aircraft Engineering and Aerospace Technology 89, no. 1 (2017): 52–65. http://dx.doi.org/10.1108/aeat-11-2014-0187.

Full text

Abstract:

Purpose The purpose of this paper is to develop and compare conventional and neural network-based controllers for gas turbines. Design/methodology/approach Design of two different controllers is considered. These controllers consist of a NARMA-L2 which is an artificial neural network-based nonlinear autoregressive moving average (NARMA) controller with feedback linearization, and a conventional proportional-integrator-derivative (PID) controller for a low-power aero gas turbine. They are briefly described and their parameters are adjusted and tuned in Simulink-MATLAB environment according to t

APA, Harvard, Vancouver, ISO, and other styles

13

PRAJAKTA, RAJENDRA GOSAVI. "DC MOTOR CONDITION MONITORING USING ARTIFICIAL NEURAL NETWORK." IJIERT - International Journal of Innovations in Engineering Research and Technology 5, no. 4 (2018): 105–9. https://doi.org/10.5281/zenodo.1454005.

Full text

Abstract:

<strong><strong> </strong>Electrical motors especially DC motor plays very important role in the industry in heavy machining industry,where starting torque plays very important role. The use of DC motor is done in a maximum way,to avail advantages of high starting toque,preferably in crane applications . The improper maintenance of industrial motors is very much necessary to avoid the production loss. In this paper artificial neural network control (ANNC) based technique is proposed f or analysis of vibration,power consumption etc. In last decade ANN based controllers even for non linear

APA, Harvard, Vancouver, ISO, and other styles

14

Rathi, K. J., and M. S. Ali. "Neural Network Controller for Power Electronics Circuits." IAES International Journal of Artificial Intelligence (IJ-AI) 6, no. 2 (2017): 49. http://dx.doi.org/10.11591/ijai.v6.i2.pp49-55.

Full text

Abstract:

Artificial Intelligence (AI) techniques, particularly the neural networks, are recently having significant impact on power electronics. This paper explores the perspective of neural network applications in the intelligent control for power electronics circuits. The Neural Network Controller (NNC) is designed to track the output voltage and to improve the performance of power electronics circuits. The controller is designed and simulated using MATLAB-SIMULINK

APA, Harvard, Vancouver, ISO, and other styles

15

Jafari, Mohammad, and Hao Xu. "Intelligent Control for Unmanned Aerial Systems with System Uncertainties and Disturbances Using Artificial Neural Network." Drones 2, no. 3 (2018): 30. http://dx.doi.org/10.3390/drones2030030.

Full text

Abstract:

Stabilizing the Unmanned Aircraft Systems (UAS) under complex environment including system uncertainties, unknown noise and/or disturbance is so challenging. Therefore, this paper proposes an adaptive neural network based intelligent control method to overcome these challenges. Based on a class of artificial neural network, named Radial Basis Function (RBF) networks an adaptive neural network controller is designed. To handle the unknown dynamics and uncertainties in the system, firstly, we develop a neural network based identifier. Then, a neural network based controller is generated based on

APA, Harvard, Vancouver, ISO, and other styles

16

Meena, Devi R., and Premalatha L. "Efficient figureconverter fed PMBLDC motor using artificial neural network." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (2019): 3025–31. https://doi.org/10.11591/ijece.v9i4.pp3025-3031.

Full text

Abstract:

In this paper, a new design of Bridgeless SEPIC (Single Ended Primary Inductance converter) with Artificial neural network (ANN) fed PMBLDC Motor drive is proposed to improve Power Factor. The proposed converter has single switching device of MOSFET, so the switching losses is reduced. ANN is used to achieve the higher power factor and fixed dc link voltage. Also the ANN methodology the time taken for computation is less since there is no mathematical model. The output voltage depends on the switching frequency of the MOSFET. The BLSEPIC act as a buck operation in continuous conduction mode. D

APA, Harvard, Vancouver, ISO, and other styles

17

Bodapati, Rakesh Babu, R. S. Srinivas, and P. V. Ramana Rao. "Artificial Neural Network-Based Hybrid Controller for Electric Vehicle Applications." WSEAS TRANSACTIONS ON CIRCUITS AND SYSTEMS 23 (October 31, 2024): 192–201. http://dx.doi.org/10.37394/23201.2024.23.20.

Full text

Abstract:

Power management among different energy sources of electric vehicles (EV) is one of the complex issues during the transition from one to another. A specific control is modeled based on the current and speed range of the electric motor named as Measurement of Parameter-Based Controller (MPBC), which will play a key role during transition of energy sources as per the load requirement. Two bidirectional converters are utilized to control the pulse signals generated by the traditional controllers which are connected at the battery and Supercapacitors (SCap) ends, which are treated as passive sourc

APA, Harvard, Vancouver, ISO, and other styles

18

Hore, Debirupa, and Runumi Sarma. "Neural network–based improved active and reactive power control of wind-driven double fed induction generator under varying operating conditions." Wind Engineering 42, no. 5 (2018): 381–96. http://dx.doi.org/10.1177/0309524x18780402.

Full text

Abstract:

Artificial neural network–based power controllers are trained using back propagation algorithm for controlling the active and reactive power of a wind-driven double fed induction generator under varying wind speed conditions and fault conditions. Vector control scheme is used for control of the double fed induction generator. Here stator flux–oriented vector control scheme is implemented for the rotor side converter and grid voltage vector scheme is used for control of grid side converter using tuned proportional–integral active and reactive power controllers, which is later replaced by artifi

APA, Harvard, Vancouver, ISO, and other styles

19

Prabhaker Reddy, Ginuga, G. Radhika, and K. Anil. "Control of Continuous Stirred Tank Reactor Using Artificial Neural Networks Based Predictive Control." Advanced Materials Research 550-553 (July 2012): 2908–12. http://dx.doi.org/10.4028/www.scientific.net/amr.550-553.2908.

Full text

Abstract:

In this work, a Neural network based predictive controller is analyzed to a non linear continuous stirred tank reactor (CSTR) carrying out series and parallel reactions: A→B→C and 2A→D. In the first step, the neural network model of continuous stirred tank reactor is obtained by Levenburg- Marquard training. The data for the training the network is generated using state space model of continuous stirred tank reactor. The neural network model of continuous stirred tank reactor is used in model predictive controller design. The performance of present neural network based model predictive control

APA, Harvard, Vancouver, ISO, and other styles

20

Jagtap, Prashant, and V. K. Chandrakar. "Intelligent method for Power flow using Artificial Neural Network with UPFC." Journal of Physics: Conference Series 2763, no. 1 (2024): 012004. http://dx.doi.org/10.1088/1742-6596/2763/1/012004.

Full text

Abstract:

Abstract It is slightly difficult to transmit such a large amount of electricity in very flexible and efficient way as the size of conventionally used relay and protective devices will increase while transmitting such a large amount of electricity which will increase the operating cost. So for transmission of such a large amount of electricity, Flexible Alternating Current Transmission Systems (FACTS) will have been found to be promising aspect in terms of the analysis of the power system. In this paper the comparative analysis of unified power flow controller(upfc) is presented in presence of

APA, Harvard, Vancouver, ISO, and other styles

21

Mathad, Vireshkumar, and Gururaj Kulkarni. "Artificial-neural-network based unified power flow controller for mitigation of power oscillations." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 3 (2021): 1323–31. https://doi.org/10.11591/ijeecs.v24.i3.pp1323-1331.

Full text

Abstract:

The series and shunt control scheme of unified power flow controller (UPFC) impacts the performance and stability of the power system during power swing. UPFC is the most versatile and voltage source converter device as it can control the real and reactive power of the transmission system simultaneously or selectively. When any system is subjected to any disturbance or fault, there are many challenges in damping power oscillation using conventional methods. This paper presents the neural network-based controller that replaces the proportional-integral (PI) controller to minimize the power osci

APA, Harvard, Vancouver, ISO, and other styles

22

BENCHIKH, Salma, Tarik JAROU, Mohamed Khalifa BOUTAHIR, Elmehdi NASRI, and Roa ELAMRANI. "Improving Photovoltaic System Performance with Artificial Neural Network Control." Data and Metadata 2 (December 30, 2023): 144. http://dx.doi.org/10.56294/dm2023144.

Full text

Abstract:

Photovoltaic systems play a pivotal role in renewable energy initiatives. To enhance the efficiency of solar panels amid changing environmental conditions, effective Maximum Power Point Tracking (MPPT) is essential. This study introduces an innovative control approach based on an Artificial Neural Network (ANN) controller tailored for photovoltaic systems. The aim is to elevate the precision and adaptability of MPPT, thereby improving solar energy harvesting. This research integrated an ANN controller into a photovoltaic system in order dynamically optimize the operating point of solar panels

APA, Harvard, Vancouver, ISO, and other styles

23

Hana Mamat, Nor, Samsul Bahari Mohd Noor, Laxshan A/L Ramar, Azura Che Soh, Farah Saleena Taip, and Ahmad Hazri Ab. Rashid. "Artificial neural network model and fuzzy logic control of dissolved oxygen in a bioreactor." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (2020): 1289. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1289-1297.

Full text

Abstract:

In a fermentation process, dissolved oxygen is the one of the key process variables that needs to be controlled because of the effect they have on the product quality. In a penicillin production, dissolved oxygen concentration influenced biomass concentration. In this paper, multilayer perceptron neural network (MLP) and Radial Basis Function (RBF) neural network is used in modeling penicillin fermentation process. Process data from an industrial scale fed-batch bioreactor is used in developing the models with dissolved oxygen and penicillin concentration as the outputs. RBF neural network mod

APA, Harvard, Vancouver, ISO, and other styles

24

Nor, Hana Mamat, Bahari Mohd Noor Samsul, A/L Ramar Laxshan, Che Soh Azura, Saleena Taip Farah, and Hazri Ab. Rashid Ahmad. "Artificial neural network model and fuzzy logic control of dissolved oxygen in a bioreactor." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 3 (2020): 1289–97. https://doi.org/10.11591/ijeecs.v17.i3.pp1289-1297.

Full text

Abstract:

In a fermentation process, dissolved oxygen is the one of the key process variables that needs to be controlled because of the effect they have on the product quality. In a penicillin production, dissolved oxygen concentration influenced biomass concentration. In this paper, multilayer perceptron neural network (MLP) and Radial Basis Function (RBF) neural network is used in modeling penicillin fermentation process. Process data from an industrial scale fed-batch bioreactor is used in developing the models with dissolved oxygen and penicillin concentration as the outputs. RBF neural network mod

APA, Harvard, Vancouver, ISO, and other styles

25

Mohammad, Ahmed Ibrahim, Saleh Alhfidh Ali, and Nathem Hamoodi Ali. "Performance enhancement of small-scale wind turbine based on artificial neural network." International Journal of Power Electronics and Drive Systems 14, no. 3 (2023): 1722~1730. https://doi.org/10.11591/ijpeds.v14.i3.pp1722-1730.

Full text

Abstract:

Small-scale wind turbine is typically designed to resisted extreme wind; this work aims to adjust their pitch angle based on simulations that use standardization codes for wind turbines. Proportional integral derivative (PID) and artificial neural network (ANN) controllers are used to control the speed of wind turbines. The ideal action for controlling the blade pitch angle can be attained by providing the controller with speed information ahead of time, allowing the controller to provide the best action for blade pitch angle control. The results of this work represent the relationship between

APA, Harvard, Vancouver, ISO, and other styles

26

Günther, Johannes, Elias Reichensdörfer, Patrick M. Pilarski, and Klaus Diepold. "Interpretable PID parameter tuning for control engineering using general dynamic neural networks: An extensive comparison." PLOS ONE 15, no. 12 (2020): e0243320. http://dx.doi.org/10.1371/journal.pone.0243320.

Full text

Abstract:

Modern automation systems largely rely on closed loop control, wherein a controller interacts with a controlled process via actions, based on observations. These systems are increasingly complex, yet most deployed controllers are linear Proportional-Integral-Derivative (PID) controllers. PID controllers perform well on linear and near-linear systems but their simplicity is at odds with the robustness required to reliably control complex processes. Modern machine learning techniques offer a way to extend PID controllers beyond their linear control capabilities by using neural networks. However,

APA, Harvard, Vancouver, ISO, and other styles

27

Jin, Fu, Jian Jun Sun, and Hong Bin Yu. "Design of Control System of Eddy Current Retarder Based on BP Neural Network PID Controller." Applied Mechanics and Materials 494-495 (February 2014): 223–28. http://dx.doi.org/10.4028/www.scientific.net/amm.494-495.223.

Full text

Abstract:

A new kind of algorithm of controller for eddy current retarder is designed in this paper. The eddy current retarder control system with traditional PID controller can't achieve a perfect performance in the rapid response. Back propagation (BP) neural network is one of artificial neural networks which has a good learning ability with a simple and recurrent structure, so it is suitable for controlling complicated eddy current retarder system. This paper introduces the principle, characteristics and learning algorithm of the BP neural network and designs the control system of eddy current retard

APA, Harvard, Vancouver, ISO, and other styles

28

EROL, HALIL, and ATAKAN ARSLAN. "WIND TURBINE PITCH ANGLE CONTROL WITH ARTIFICIAL NEURAL NETWORKS." REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE 70, no. 2 (2025): 235–40. https://doi.org/10.59277/rrst-ee.2025.2.14.

Full text

Abstract:

Pitch angle control in wind turbines is required to obtain maximum efficiency from wind turbines at variable wind speeds. Since the wind turbine pitch control structure is not linear, the control cannot be fully achieved, and oscillations occur at the power output. This oscillation can increase because the pitch angle cannot be adjusted stably. This study employs pitch angle control using artificial neural networks, a proportional-integral-derivative (PID) controller, and adaptive neuro-fuzzy inference systems (ANFIS) methods. When the artificial neural network, PID, and ANFIS outputs are comp

APA, Harvard, Vancouver, ISO, and other styles

29

Machavarapu, Suman, Mannam Venu Gopala Rao, and Pulipaka Venkata Ramana Rao. "Design of Load Frequency Controller for Multi-area System Using AI Techniques." Journal Européen des Systèmes Automatisés 53, no. 4 (2020): 541–48. http://dx.doi.org/10.18280/jesa.530413.

Full text

Abstract:

The paper presents an adaptive Load Frequency Controller (LFC) based on a neural network for the interconnected multi-area systems. When there is an imbalance between active power generation and demand there will deviation in the frequency from the reference value. Major disturbances that lead to the variation in frequency beyond the allowable limits are variation in load demand and faults, etc. Initially PID based LFC which is a conventional controller is used to bring back the variations in frequency when there is a disturbance. But these conventional controllers will operate certain operati

APA, Harvard, Vancouver, ISO, and other styles

30

Mahdi, Shaymaa Mahmood, and Omar Farouq Lutfy. "Control of a servo-hydraulic system utilizing an extended wavelet functional link neural network based on sine cosine algorithms." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 847–56. https://doi.org/10.11591/ijeecs.v25.i2.pp847-856.

Full text

Abstract:

Servo-hydraulic systems have been extensively employed in various industrial applications. However, these systems are characterized by their highly complex and nonlinear dynamics, which complicates the control design stage of such systems. In this paper, an extended wavelet functional link neural network (EWFLNN) is proposed to control the displacement response of the servo-hydraulic system. To optimize the controller's parameters, a recently developed optimization technique, which is called the modified sine cosine algorithm (M-SCA), is exploited as the training method. The proposed contr

APA, Harvard, Vancouver, ISO, and other styles

31

Sumit Saroha, Vinita, Abhimanyu,. "Artificial Neural Network Based Solar Prediction in Multi-Area Networks." Tuijin Jishu/Journal of Propulsion Technology 44, no. 3 (2023): 2548–56. http://dx.doi.org/10.52783/tjjpt.v44.i3.746.

Full text

Abstract:

A clean, readily accessible and renewable energy source is solar energy. The use of photovoltaic panels is a popular trend to harness solar energy for the production of electrical power. Variable generated electricity is a result of solar energy's intermittent nature. An energy management system with a solar forecast module is presented in this study. The main purpose of this study is to apply ANN for verifying and predicting solar energy in India. The output is controlled by an ANN controller. To meet the demands put forward by the Energy Management System (EMS), artificial neural networks (A

APA, Harvard, Vancouver, ISO, and other styles

32

Ibrahim, Mohammad Ahmed, Ali Saleh Saleh, and Ali Nathem Hamoody. "Performance enhancement of small-scale wind turbine based on artificial neural network." International Journal of Power Electronics and Drive Systems (IJPEDS) 14, no. 3 (2023): 1722. http://dx.doi.org/10.11591/ijpeds.v14.i3.pp1722-1730.

Full text

Abstract:

<p><span lang="EN-US">Small-scale wind turbine is typically designed to resisted extreme wind; this work aims to adjust their pitch angle based on simulations that use standardization codes for wind turbines. Proportional integral derivative (PID) and artificial neural network (ANN) controllers are used to control the speed of wind turbines. The ideal action for controlling the blade pitch angle can be attained by providing the controller with speed information ahead of time, allowing the controller to provide the best action for blade pitch angle control. The results of this work

APA, Harvard, Vancouver, ISO, and other styles

33

Bondar, Oleksiy. "Predictive Neural Network in Multipurpose Self-Tuning Controller." Acta Mechanica et Automatica 14, no. 2 (2020): 114–20. http://dx.doi.org/10.2478/ama-2020-0017.

Full text

Abstract:

AbstractA very important problem in designing of controlling systems is to choose the right type of architecture of controller. And it is always a compromise between accuracy, difficulty in setting up, technical complexity and cost, expandability, flexibility and so on. In this paper, multipurpose adaptive controller with implementation of artificial neural network is offered as an answer to a wide range of tasks related to regulation. The effectiveness of the approach is demonstrated by the example of an adaptive thermostat. It also compares its capabilities with those of classic PID controll

APA, Harvard, Vancouver, ISO, and other styles

34

Uhumnwangho, R., E. Omorogiuwa, and G. Offor. "VOLTAGE COMPENSATION USING ARTIFICIAL NEURAL NETWORK: A CASE STUDY OF RUMUOLA DISTRIBUTION NETWORK." Nigerian Journal of Technology 36, no. 1 (2016): 178–85. http://dx.doi.org/10.4314/njt.v36i1.23.

Full text

Abstract:

A study of hourly voltage log taken over a period of six months from Rumuola Distribution network Port Harcourt, Rivers State indicates that power quality problems prevalent in the Network are undervoltage/voltage sags and overvoltage/voltage swells. This paper aims at addressing these power quality problems in the distribution network using artificial neural network (ANN) controller based dynamic voltage restorer (DVR). The artificial neural networks controller engaged to controlling the dynamic voltage restorer were trained with input and output data of proportional integral (PI) controller

APA, Harvard, Vancouver, ISO, and other styles

35

Lan, Ming-Shong, P. Lin, and J. Bain. "Modeling and Control of the Lithographic Offset Color Printing Process Using Artificial Neural Networks." Journal of Engineering for Industry 116, no. 2 (1994): 274–76. http://dx.doi.org/10.1115/1.2901942.

Full text

Abstract:

This paper investigates the use of artificial neural networks (ANNs) for modeling and control of the lithographic offset color printing process. The color controller consists of two ANNs; the controller network, which learns an inverse model of the process, takes a set of desired colors as input and generates a set of ink key settings, while the model network learns a forward model of the process through which the controller network can be adapted by using the error backpropagation method. We use three-layer networks with “local” connections between neurons of adjacent layers for the process m

APA, Harvard, Vancouver, ISO, and other styles

36

Arins, Gustavo, and Benjamin Grando Moreira. "Controlador neural para um motor CC: aspectos de projeto e simulação." Anais do Computer on the Beach 14 (May 3, 2023): 032–39. http://dx.doi.org/10.14210/cotb.v14.p032-039.

Full text

Abstract:

ABSTRACTThiswork presents the development of a neural controller to controlthe speed of a CC motor, applying as a priority the response time,the overshoot percentage, and the required precision of the system.We developed a simulation of two alternatives for control: a PIDcontroller and a controller using Artificial Neural Networks. Toselect the network architeture quantitative and qualitative analyzeswere carried out to interpret the phenomena and the behavior ofeach proposed network architecture.

APA, Harvard, Vancouver, ISO, and other styles

37

Banda, Gururaj, and Sri Gowri Kolli. "An Intelligent Adaptive Neural Network Controller for a Direct Torque Controlled eCAR Propulsion System." World Electric Vehicle Journal 12, no. 1 (2021): 44. http://dx.doi.org/10.3390/wevj12010044.

Full text

Abstract:

This article deals with an intelligent adaptive neural network (ANN) controller for a direct torque controlled (DTC) electric vehicle (EV) propulsion system. With the realization of artificial intelligence (AI) conferred adaptive controllers, the torque control of an electric car (eCAR) propulsion motor can be achieved by estimating the stator reference flux voltage used to synthesize the space vector pulse width modulation (SVPWM) for a DTC scheme. The proposed ANN tool optimizes the parameters of a proportional integral (PI) controller with real-time data and offers splendid dynamic stabilit

APA, Harvard, Vancouver, ISO, and other styles

38

Abd Kadir, Mahamad, and Saon Sharifah. "Development of Artificial Neural Network Based MPPT for Photovoltaic System during Shading Condition." Applied Mechanics and Materials 448-453 (October 2013): 1573–78. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.1573.

Full text

Abstract:

This paper presents Feedforward Neural network (FFNN) and Elman network controllers to control the maximum power point tracking (MPPT) of photovoltaic (PV). MPPT is a method used to extract the maximum available power from photovoltaic module by designs them to operate efficiently. Thus, cell temperatures and solar irradiances are two critical variable factors to determine PV output powers. The performances of the controller is analyzed in four conditions which are i) constant irradiation and temperature, ii) constant irradiation and variable temperature, iii) constant temperature and variable

APA, Harvard, Vancouver, ISO, and other styles

39

Jawad, Raheel, Majda Ahmed, Hussein Salih, and Yasser Mahmood. "Variable Speed Controller of Wind Generation System using Model predictive Control and NARMA Controller." Iraqi Journal for Electrical and Electronic Engineering 18, no. 2 (2022): 43–52. http://dx.doi.org/10.37917/ijeee.18.2.6.

Full text

Abstract:

This paper applied an artificial intelligence technique to control Variable Speed in a wind generator system. One of these techniques is an offline Artificial Neural Network (ANN-based system identification methodology, and applied conventional proportional-integral-derivative (PID) controller). ANN-based model predictive (MPC) and remarks linearization (NARMA-L2) controllers are designed and employed to manipulate Variable Speed in the wind technological knowledge system. All parameters of controllers are set up by the necessities of the controller’s design. The effects show a neural local (N

APA, Harvard, Vancouver, ISO, and other styles

40

Sekaj, Ivan, Ivan Kénický, and Filip Zúbek. "Neuro-Evolution of Continuous-Time Dynamic Process Controllers." MENDEL 27, no. 2 (2021): 7–11. http://dx.doi.org/10.13164/mendel.2021.2.007.

Full text

Abstract:

Artificial neural networks are means which are, among several other approaches, effectively usable for modelling and control of non-linear dynamic systems. In case of modelling systems input and output signals are a-priori known, supervised learning methods can be used. But in case of controller design of dynamic systems the required (optimal) controller output is a-priori unknown, supervised learning cannot be used. In such case we only can define some criterion function, which represents the required control performance of the closed-loop system. We present a neuro-evolution design for contr

APA, Harvard, Vancouver, ISO, and other styles

41

Mahdi, Shaymaa Mahmood, and Omar Farouq Lutfy. "Control of a servo-hydraulic system utilizing an extended wavelet functional link neural network based on sine cosine algorithms." Indonesian Journal of Electrical Engineering and Computer Science 25, no. 2 (2022): 847. http://dx.doi.org/10.11591/ijeecs.v25.i2.pp847-856.

Full text

Abstract:

Servo-hydraulic systems have been extensively employed in various industrial applications. However, these systems are characterized by their highly complex and nonlinear dynamics, which complicates the control design stage of such systems. In this paper, an extended wavelet functional link neural network (EWFLNN) is proposed to control the displacement response of the servo-hydraulic system. To optimize the controller's parameters, a recently developed optimization technique, which is called the modified sine cosine algorithm (M-SCA), is exploited as the training method. The proposed controlle

APA, Harvard, Vancouver, ISO, and other styles

42

Mystkowski, Arkadiusz, Adam Wolniakowski, Nesrine Kadri, Mateusz Sewiolo, and Lorenzo Scalera. "Neural Network Learning Algorithms for High-Precision Position Control and Drift Attenuation in Robotic Manipulators." Applied Sciences 13, no. 19 (2023): 10854. http://dx.doi.org/10.3390/app131910854.

Full text

Abstract:

In this paper, different learning methods based on Artificial Neural Networks (ANNs) are examined to replace the default speed controller for high-precision position control and drift attenuation in robotic manipulators. ANN learning methods including Levenberg–Marquardt and Bayesian Regression are implemented and compared using a UR5 robot with six degrees of freedom to improve trajectory tracking and minimize position error. Extensive simulation and experimental tests on the identification and control of the robot by means of the neural network controllers yield comparable results with respe

APA, Harvard, Vancouver, ISO, and other styles

43

Mahdi, Hussain. "POSITION CONTROL FOR FLIXEBLE JOINT MANIPULATOR USING ARTIFICIAL NEURAL Network." Diyala Journal of Engineering Sciences 1, no. 1 (2008): 122–38. http://dx.doi.org/10.24237/djes.2008.01109.

Full text

Abstract:

The control of a rotating single flexible link manipulator and/or a two-coupled flexible link manipulator arm is a highly nonlinear problem. Due to the distributed flexibility. The Mechanical system of a flexible joint two-degree manipulator robot arm has been designed and implemented by using stepper motor, movement axis and External Model Circuit (EMC) for controller. The (EMC) includes Buffer, stepper motor driver and programmable Input/Output. This system is controlled by using two method .The first is Artificial Neural Networks (ANN). The neural network has a feed-forward topology and lea

APA, Harvard, Vancouver, ISO, and other styles

44

Efrati, T., and H. Flashner. "Neural Network Based Tracking Control of Mechanical Systems." Journal of Dynamic Systems, Measurement, and Control 121, no. 1 (1999): 148–54. http://dx.doi.org/10.1115/1.2802435.

Full text

Abstract:

A method for tracking control of mechanical systems based on artificial neural networks is presented. The controller consists of a proportional plus derivative controller and a two-layer feedforward neural network. It is shown that the tracking error of the closed-loop system goes to zero while the control effort is minimized. Tuning of the neural network’s weights is formulated in terms of a constrained optimization problem. The resulting algorithm has a simple structure and requires a very modest computation effort. In addition, the neural network’s learning procedure is implemented on-line.

APA, Harvard, Vancouver, ISO, and other styles

45

Arjun, Joshi, Gaganambha, B. G. Dharshan, et al. "Comparative analysis of Dynamic Voltage Restorer based on PI and ANN Control strategies in order to improve the voltage quality under Non-linear loads." World Journal of Advanced Research and Reviews 22, no. 3 (2024): 292–303. https://doi.org/10.5281/zenodo.14725906.

Full text

Abstract:

Voltage sag, swell and harmonic distortion are the key power quality concerns addressed by the distribution network. The presence of non-linear loads and renewable energy sources like solar, wind etc. is the reason for power quality issues. These necessitates the need for the development of a power quality conditioner to compensate the effects of these power quality problems. Hence a Dynamic Voltage Restorer is developed and deployed to improve power quality by decreasing harmonics and adjusting for voltage swell and sag. DVR control is achieved by regulating the load voltage under a variety o

APA, Harvard, Vancouver, ISO, and other styles

46

Bai, Meng, and Min Hua Li. "A Neural Network Method for Miniature Unmanned Helicopter Heading Control." Advanced Materials Research 468-471 (February 2012): 93–96. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.93.

Full text

Abstract:

A neural network control method for heading control of miniature unmanned helicopter is proposed. For the complexity of miniature helicopter aerodynamics, it is difficult to identify the unknown parameters of yaw dynamics model. To design heading controller of miniature helicopter without modelling yaw dynamics, BP neural network is designed as heading controller, which is trained by collected flight data. By training, the neural network controller can learn the artificial operation strategy and realize the heading control of miniature unmanned helicopter. Simulation results demonstrate the va

APA, Harvard, Vancouver, ISO, and other styles

47

Yuan, Zhi. "A Neural Network Based Space-Vector PWM Controller for Motor Drive." Advanced Materials Research 816-817 (September 2013): 1002–5. http://dx.doi.org/10.4028/www.scientific.net/amr.816-817.1002.

Full text

Abstract:

This paper proposed an artificial neural network (ANN) based space vector pulse width modulation (SVPWM) for motor drive which fully covers the undermodulation and overmodulation regions. A neural network has the advantage of very fast implementation of an SVPWM algorithm that can increase the converter switching frequency, particularly when a dedicated application-specific integrated circuit chip is used in the modulator. Finally, in the environment of MATLAB/Simulink with the Neural Network Toolbox builds the simulation model of system with proposed ANN-SVPWM controller. The simulation resul

APA, Harvard, Vancouver, ISO, and other styles

48

Ibarra, José Ramón Meza, Joaquın Martınez Ulloa, Luis Alfonso Moreno Pacheco, and Hugo Rodrıguez Cortes. "Altitude Controller Based on Artificial Neural Network Genetic Algorithm for a Quadcopter MAV." International Journal of Robotics and Control Systems 4, no. 4 (2024): 1862–85. https://doi.org/10.31763/ijrcs.v4i4.1582.

Full text

Abstract:

Mechanical systems with high dynamic complexity often face challenges due to unmodeled uncertainties and external perturbations, making effective control difficult. Therefore, new advanced, robust, intelligent control theories have been developed through the sudden advance of computational power in recent years. In this research work, these new theories of automatic control are used, mainly based on what is currently called Artificial Intelligence (AI) algorithms, to develop a novel altitude controller based on the theory of Genetic Algorithms (GA) and Artificial Neural Networks (ANN).Theperfo

APA, Harvard, Vancouver, ISO, and other styles

49

Sánchez-Ruiz, Francisco Javier, Elizabeth Argüelles Hernandez, José Terrones-Salgado, and Luz Judith Fernández Quiroz. "Evolutionary artificial neural network for temperature control in a batch polymerization reactor." Ingenius, no. 30 (July 1, 2023): 79–89. http://dx.doi.org/10.17163/ings.n30.2023.07.

Full text

Abstract:

The integration of artificial intelligence techniques introduces fresh perspectives in the implementation of these methods. This paper presents the combination of neural networks and evolutionary strategies to create what is known as evolutionary artificial neural networks (EANNs). In the process, the excitation function of neurons was modified to allow asexual reproduction. As a result, neurons evolved and developed significantly. The technique of a batch polymerization reactor temperature controller to produce polymethylmethacrylate (PMMA) by free radicals was compared with two different con

APA, Harvard, Vancouver, ISO, and other styles

50

Abougarair, Ahmed Jaber, Mohamed K. I. Aburakhis, and Mohamed M. Edardar. "Adaptive Neural Networks Based Robust Output Feedback Controllers for Nonlinear Systems." International Journal of Robotics and Control Systems 2, no. 1 (2022): 37–56. http://dx.doi.org/10.31763/ijrcs.v2i1.523.

Full text

Abstract:

The performance of the nonlinear control system that is subjected to uncertainty, can be enhanced by implementing an adaptive approach by using the robust output-feedback control and the artificial intelligence neural network. This paper seeks to utilize output feedback control for nonlinear system using artificial intelligence employing neural network. The Two Wheel Mobile Robot (TWMR) is treated as a multi-body dynamic system. The nonlinear swing-up problem is handled by designing an adaptive neural network, which is trained using a modified conventional controller called Linear Quadratic Op

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!