Speed Control Of Brushless Dc Motor By Neural Network Pid Controller
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| Author | : Amandeep Gill |
| Publisher | : LAP Lambert Academic Publishing |
| Total Pages | : 92 |
| Release | : 2013 |
| Genre | : |
| ISBN | : 9783659397660 |
The aim of the book is to design a simulation model of Brushless dc motor and to control its speed at different values of load torques.In this light, new control schemes should be devised for a better solution of a non linear system. Recently, work has been started toward the development of Artificial Neural Network (ANN) based intelligent controllers. The ANN has several key features that make it highly suitable for BLDCM speed applications. The ANN based PID controller is used for the speed control of BLDCM at different values of load torque and its comparison is done with the conventional controllers like PID and PI controllers.
| Author | : Yasir Al-Yasir |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2019 |
| Genre | : Technology & Engineering |
| ISBN | : |
In the recent years, researchers have sophisticated the synthesis of neural networks depending on the wavelet functions to build the wavelet neural networks (WNNs), where the wavelet function is utilized in the hidden layer as a sigmoid function instead of conventional sigmoid function that is utilized in artificial neural network. The WNN inherits the features of the wavelet function and the neural network (NN), such as self-learning, self-adapting, time-frequency location, robustness, and nonlinearity. Besides, the wavelet function theory guarantees that the WNN can simulate the nonlinear system precisely and rapidly. In this chapter, the WNN is used with PID controller to make a developed controller named WNN-PID controller. This controller will be utilized to control the speed of Brushless DC (BLDC) motor to get preferable performance than the traditional controller techniques. Besides, the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of the WNN-PID controller. The modification for this method of the WNN such as the recurrent wavelet neural network (RWNN) was included in this chapter. Simulation results for all the above methods are given and compared.
| Author | : Mustefa Jibril |
| Publisher | : GRIN Verlag |
| Total Pages | : |
| Release | : 2020-05-11 |
| Genre | : Computers |
| ISBN | : 3346164179 |
Academic Paper from the year 2020 in the subject Computer Science - Miscellaneous, , language: English, abstract: In this paper we describe a technical system for DC motor speed control. The speed of DC motor is controlled using Neural Network Based Model Reference and Predictive controllers with the use of Matlab/Simulink. The analysis of the DC motor is done with and without input side Torque disturbance input and the simulation results obtained by comparing the desired and actual speed of the DC motor using random reference and sinusoidal speed inputs for the DC motor with Model Reference and Predictive controllers. The DC motor with Model Reference controller shows almost the actual speed is the same as the desired speed with a good performance than the DC motor with Predictive controller for the system with and without input side disturbance. Finally the comparative simulation result prove the effectiveness of the DC motor with Model Reference controller.
| Author | : Chang-liang Xia |
| Publisher | : John Wiley & Sons |
| Total Pages | : 306 |
| Release | : 2012-04-24 |
| Genre | : Technology & Engineering |
| ISBN | : 1118188365 |
An advanced introduction to the simulation and hardware implementation of BLDC motor drives A thorough reference on the simulation and hardware implementation of BLDC motor drives, this book covers recent advances in the control of BLDC motor drives, including intelligent control, sensorless control, torque ripple reduction and hardware implementation. With the guidance of the expert author team, readers will understand the principle, modelling, design and control of BLDC motor drives. The advanced control methods and new achievements of BLDC motor drives, of interest to more advanced readers, are also presented. Focuses on the control of PM brushless DC motors, giving readers the foundations to the topic that they can build on through more advanced reading Systematically guides readers through the subject, introducing basic operational principles before moving on to advanced control algorithms and implementations Covers special issues, such as sensorless control, intelligent control, torque ripple reduction and hardware implementation, which also have applications to other types of motors Includes presentation files with lecture notes and Matlab 7 coding on a companion website for the book
| Author | : J. R. Hendershot |
| Publisher | : Clarendon Press |
| Total Pages | : 0 |
| Release | : 1994 |
| Genre | : Electric motors, Brushless |
| ISBN | : 9780198593898 |
Brushless permanent-magnet motors provide simple, low maintenance, and easily controlled mechanical power. Written by two leading experts on the subject, this book offers the most comprehensive guide to the design and performance of brushless permanent-magnetic motors ever written. Topics range from electrical and magnetic design to materials and control. Throughout, the authors stress both practical and theoretical aspects of the subject, and relate the material to modern software-based techniques for design and analysis. As new magnetic materials and digital power control techniques continue to widen the scope of the applicability of such motors, the need for an authoritative overview of the subject becomes ever more urgent. Design of Brushless Permanent-Magnet Motors fits the bill and will be read by students and researchers in electric and electronic engineering.
| Author | : Constantin Volosencu |
| Publisher | : BoD – Books on Demand |
| Total Pages | : 422 |
| Release | : 2021-04-21 |
| Genre | : Technology & Engineering |
| ISBN | : 1839627131 |
The book presents recent theoretical and practical information about the field of automation and control. It includes fifteen chapters that promote automation and control in practical applications in the following thematic areas: control theory, autonomous vehicles, mechatronics, digital image processing, electrical grids, artificial intelligence, and electric motor drives. The book also presents and discusses applications that improve the properties and performances of process control with examples and case studies obtained from real-world research in the field. Automation and Control is designed for specialists, engineers, professors, and students.
| Author | : |
| Publisher | : |
| Total Pages | : 45 |
| Release | : 2011 |
| Genre | : DC-to-DC converters |
| ISBN | : |
| Author | : Norhaliza Abdul Wahab |
| Publisher | : Springer Nature |
| Total Pages | : 880 |
| Release | : 2022-07-07 |
| Genre | : Technology & Engineering |
| ISBN | : 9811939233 |
This proceeding includes original and peer-reviewed research papers from the 3rd International Conference on Control, Instrumentation and Mechatronics Engineering (CIM2022). The conference is a virtual conference held on 2-3 March 2022. The topics covered latest work and finding in the area of Control Engineering, Mechatronics, Robotics and Automation, Artificial Intelligence, Manufacturing, Sensor, Measurement and Instrumentation. Moreover, the latest applications of instrumentations, control and mechatronics are provided. Therefore, this proceeding is a valuable material for researchers, academicians, university students and engineers.
| Author | : Pritesh Shah |
| Publisher | : CRC Press |
| Total Pages | : 301 |
| Release | : 2021-09-28 |
| Genre | : Computers |
| ISBN | : 1000435946 |
Due to increasing industry 4.0 practices, massive industrial process data is now available for researchers for modelling and optimization. Artificial Intelligence methods can be applied to the ever-increasing process data to achieve robust control against foreseen and unforeseen system fluctuations. Smart computing techniques, machine learning, deep learning, computer vision, for example, will be inseparable from the highly automated factories of tomorrow. Effective cybersecurity will be a must for all Internet of Things (IoT) enabled work and office spaces. This book addresses metaheuristics in all aspects of Industry 4.0. It covers metaheuristic applications in IoT, cyber physical systems, control systems, smart computing, artificial intelligence, sensor networks, robotics, cybersecurity, smart factory, predictive analytics and more. Key features: Includes industrial case studies. Includes chapters on cyber physical systems, machine learning, deep learning, cybersecurity, robotics, smart manufacturing and predictive analytics. surveys current trends and challenges in metaheuristics and industry 4.0. Metaheuristic Algorithms in Industry 4.0 provides a guiding light to engineers, researchers, students, faculty and other professionals engaged in exploring and implementing industry 4.0 solutions in various systems and processes.
| Author | : |
| Publisher | : |
| Total Pages | : |
| Release | : |
| Genre | : |
| ISBN | : |
This paper uses Artificial Neural Networks (ANNs) in estimating speed and controlling it for a separately excited DC motor. The rotor speed of the dc motor can be made to follow an arbitrarily selected trajectory. The purpose is to achieve accurate trajectory control of the speed, especially when the motor and load parameters are unknown. Such a neural control scheme consists of two parts. One is the neural identifier which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neural networks are trained by Levenberg-Marquardt back-propagation algorithm. ANNs used in this are the standard three layers feedforward neural network with sigmoid activation functions in the input and hidden layers while linear activation function is employed for the output layer. The conventional constant gain feedback controller fails to maintain the performance of the system at acceptable levels under unknown dynamics in load torque. On the other hand, ANNs act as an effective tool to implement the model and adaptive control in a complicated non-linear system having expansive allocations. The adaptive learning algorithm is formed in such a way that the learning rate is as large as possible while maintaining the stability of the learning process. This simplifies the learning process in terms of computation time, which is of special importance in real-time implementation.
