Azuwien Aida Bohari

Vector Control of Induction Motor Using Neural Network

This paper deals with field oriented control of induction motor drive system with an online neural network for speed control. The field oriented control used to decoupling the flux and torque in order to get the performance as well as direct current motor. The online neural network is designed to maintain the output speed variation. To verify the effectiveness of the proposed method, a simulation model was developed. The result shows that the performance of transient response is improved in term of overshoot and settling time by using neural network field oriented control system. It is concluded that neural network based field oriented control schemes of induction motor drive is more effective to replace the conventional proportional integral derivative based field oriented control technique.

Induction Motor Drive Based Neural Network Direct Torque Control

A neural network based direct torque control of an induction motor was presented in this paper. The paper trained a neural network for speed controller of the machine to use in the feed-back loop of the control system. The description of the control system, training procedure of the neural network is given in this paper. The complete neural network based direct torque control scheme of induction motor drive is simulated using MATLAB. The acquired results compared with the conventional direct torque control reveal the effectiveness of the neural network based direct torque control schemes of induction motor drives. The proposed scheme improved the performance of transient response by reduces the overshoot. The validity of the proposed method is verified by the simulation results.

Enhance Cascaded H-Bridge Multilevel Inverter with Artificial Intelligence Control

Received Dec 04, 2017 Revised Jan 11, 2018 Accepted Apr 15, 2018 Wireless Body Area Networks (WBANs) are fundamental technology in health care that permits the information of a patient’s essential body parameters to be gathered by the sensors. However, the safety and concealment defense of the gathered information is a key uncertain problem. A Hybrid Key Management (HKM) scheme [13] is worked based on Public Key Cryptography (PKC)-authentication scheme. This scheme uses a oneway hash function to construct a Merkle Tree. The PKC method increase the computational complexity and lacking scalability. Additionally, it increases expensive computation, communication costs and delay. To overcome this problem, Robust Security for Protected Health Information by ECC with signature Hash Function in WBAN (RSP) is proposed. The system employs hash-chain based key signature technique to achieve efficient, secure transmission from sensor to user in WBAN. Moreover, Elliptical Curve Cryptography algorithm is used to verifies the authenticate sensor. In addition, it describes the experimental results of the proposed system demonstrate the efficient data communication in a network.A Weblogs contains the history of User Navigation Pattern while user accessing the websites. The user navigation pattern can be analyzed based on the previous user navigation that is stored in weblog. The weblog comprises of various entries like IP address, status code and number of bytes transferred, categories and time stamp. The user interest can be classified based on categories and attributes and it is helpful in identifying user behavior. The aim of the research is to identifying the interested user behavior and not interested user behavior based on classification. The process of identifying user interest, it consists of Modified Span Algorithm and Personalization Algorithm based on the classification algorithm user prediction can be analyzed. The research work explores to analyze user prediction behavior based on user personalization that is captured from weblogs.Wireless Body Area Networks (WBANs) are fundamental technology in health care that permits the information of a patient’s essential body parameters to be gathered by the sensors. However, the safety and concealment defense of the gathered information is a key uncertain problem. A Hybrid Key Management (HKM) scheme [13] is worked based on Public Key Cryptography (PKC)-authentication scheme. This scheme uses a oneway hash function to construct a Merkle Tree. The PKC method increase the computational complexity and lacking scalability. Additionally, it increases expensive computation, communication costs and delay. To overcome this problem, Robust Security for Protected Health Information by ECC with signature Hash Function in WBAN (RSP) is proposed. The system employs hash-chain based key signature technique to achieve efficient, secure transmission from sensor to user in WBAN. Moreover, Elliptical Curve Cryptography algorithm is used to verifies the authenticate sensor. In addition, it describes the experimental results of the proposed system demonstrate the efficient data communication in a network.

Neural network SVPWM-DTC of induction motor for EV load model

A three phases induction motor is used as an electric vehicle propulsion system in this paper. The proposed neural network speed controller is design based on the space vector modulation technique on direct torque control. Since the electric drive performance significantly lean against on the design of speed controller, thus the improvement on the speed controller become the core of this research and so it is enhanced by replace the generic speed controller by the adaptive neural network controller. The performance of the control system in addition with the neural network learning scheme are depict in this paper. The complete schemes of both conventional and proposed scheme are simulated using Mathlab. The comparison system performance between the conventional direct torque control and the proposed intelligent neural network speed control have been investigated and show a satisfy result in both steady state and transient response. The simulation results verify that the proposed direct torque controller with the adaptive speed controller for induction motor satisfy and effectiveness enough as the candidate for electric vehicle propulsion.

Speed Control of Permanent Magnet Synchronous Motor Using FOC Neural Network

This paper presents the performance analysis of the field oriented control for a permanent magnet synchronous motor drive with a proportional-integral-derivative and artificial neural network controller in closed loop operation. The mathematical model of permanent magnet synchronous motor and artificial neural network algorithm is derived. While, the current controlled voltage source inverter feeding power to the motor is powered from space vector pulse width modulation current controlled converter. The effectiveness of the proposed method is verified by develop simulation model in MATLAB-Simulink program. The simulation results prove the proposed artificial neural network controller produce significant improvement control performance compare to the proportional-integral-derivative controller for both condition controlling speed reference variations and constant load. It can conclude that by using proposed controller, the overshoot, steady state error and rise time can be reducing significantly.