Speed Control of Separately Excited DC Motor Using Artificial Intelligent Approach PDF Download

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Languages : en
Pages : 46

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This paper uses NEURO FUZZY TECHNIQUE 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 of saperately excited DC Motor, especially when the motor and load parameters are unknown. Such a neuro fuzzy control scheme consists of two parts. One is the neural identifier which is used to estimate the motor speed error (state error or state error derivative). The second is the fuzzy logic controller which is used to generate a control signal for a chopper & speed control of separately excited DC Motor. The purpose of this technique is to achieve accurate trajectory control of the speed. Such a control scheme consists of two parts. One is the neural identifier which is used to estimate the motor speed. The other is the neural fuzzy logic controller which is used to generate the control signal (fuzzy output).

Author: Valentina E. Balas
Publisher: Springer Nature
ISBN: 3030326446
Category : Technology & Engineering
Languages : en
Pages : 618

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This book covers all the emerging trends in artificial intelligence (AI) and the Internet of Things (IoT). The Internet of Things is a term that has been introduced in recent years to define devices that are able to connect and transfer data to other devices via the Internet. While IoT and sensors have the ability to harness large volumes of data, AI can learn patterns in the data and quickly extract insights in order to automate tasks for a variety of business benefits. Machine learning, an AI technology, brings the ability to automatically identify patterns and detect anomalies in the data that smart sensors and devices generate, and it can have significant advantages over traditional business intelligence tools for analyzing IoT data, including being able to make operational predictions up to 20 times earlier and with greater accuracy than threshold-based monitoring systems. Further, other AI technologies, such as speech recognition and computer vision can help extract insights from data that used to require human review. The powerful combination of AI and IoT technology is helping to avoid unplanned downtime, increase operating efficiency, enable new products and services, and enhance risk management.

Author: Evelio J. González
Publisher: Bentham Science Publishers
ISBN: 1608051269
Category : Technology & Engineering
Languages : en
Pages : 205

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"This e-book focuses on the application of artificial intelligence resources in fields related to Control and Automation Engineering. Techniques such as neural networks, fuzzy logic and expert systems are a key tool for researchers and engineers requiring "

Author: Chitra A.
Publisher: John Wiley & Sons
ISBN: 1119681901
Category : Computers
Languages : en
Pages : 288

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Electric vehicles are changing transportation dramatically and this unique book merges the many disciplines that contribute research to make EV possible, so the reader is informed about all the underlying science and technologies driving the change. An emission-free mobility system is the only way to save the world from the greenhouse effect and other ecological issues. This belief has led to a tremendous growth in the demand for electric vehicles (EV) and hybrid electric vehicles (HEV), which are predicted to have a promising future based on the goals fixed by the European Commission's Horizon 2020 program. This book brings together the research that has been carried out in the EV/HEV sector and the leading role of advanced optimization techniques with artificial intelligence (AI). This is achieved by compiling the findings of various studies in the electrical, electronics, computer, and mechanical domains for the EV/HEV system. In addition to acting as a hub for information on these research findings, the book also addresses the challenges in the EV/HEV sector and provides proven solutions that involve the most promising AI techniques. Since the commercialization of EVs/HEVs still remains a challenge in industries in terms of performance and cost, these are the two tradeoffs which need to be researched in order to arrive at an optimal solution. Therefore, this book focuses on the convergence of various technologies involved in EVs/HEVs. Since all countries will gradually shift from conventional internal combustion (IC) engine-based vehicles to EVs/HEVs in the near future, it also serves as a useful reliable resource for multidisciplinary researchers and industry teams.

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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.

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Languages : en
Pages : 68

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Author: Darina Dicheva
Publisher: Springer
ISBN: 364215431X
Category : Computers
Languages : en
Pages : 299

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The 14th International Conference on Artificial Intelligence: Methodology, Systems, Applications (AIMSA 2010) was held in Varna, Bulgaria, during September 8–10, 2010. The AIMSA conference series has provided a biennial forum for the presen- tion of artificial intelligence research and development since 1984. The conference covers the full range of topics in artificial intelligence (AI) and related disciplines and provides an ideal forum for international scientific exchange between Central/Eastern Europe and the rest of the world. The 2010 AIMSA edition continued this tradition. For AIMSA 2010, we decided to place special emphasis on the application and leverage of AI technologies in the context of knowledge societies where knowledge creation, accessing, acquiring, and sharing empower individuals and communities. A number of AI techniques play a key role in responding to these challenges. AI is - tensively used in the development of systems for effective management and flexible and personalized access to large knowledge bases, in the Semantic Web technologies that enable sharing and reuse of and reasoning over semantically annotated resources, in the emerging social Semantic Web applications that aid humans to collaboratively build semantics, in the construction of intelligent environments for supporting (human and agent) learning, etc. In building such intelligent applications, AI techniques are typically combined with results from other disciplines such as the social sciences, distributed systems, databases, digital libraries, information retrieval, service-oriented applications, etc.

Author: Gilberto José Martínez Pino
Publisher:
ISBN:
Category : Electric motors, Direct current
Languages : en
Pages : 456

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Author: Mustefa Jibril
Publisher: GRIN Verlag
ISBN: 3346164179
Category : Computers
Languages : en
Pages :

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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.