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 current and speed of the motor. In this paper performance of the indirect vector controller containing artificial neural network based fuzzy logic (ANFIS) based control system is studied and compared with regular fuzzy logic system, which is developed without using artificial neural network. Data required to model the artificial neural network based fuzzy inference system is obtained from the PI controlled induction motor system. Results obtained in MATLAB-SIMULINK simulation shows that the ANFIS controller is superior compared to controller which is implemented only using fuzzy logic, under all dynamic conditions.

Brushless dc motor is finding various applications in the present day scenario. They have improved efficiency, higher speed, better torque speed characteristics and faster dynamic response when compared with the traditional motors. In this paper, a controller is developed based on co-simulation of multi sim and lab view and is presented for low cost brushless dc motor drive with low-resolution hall sensors. The driver circuit is made using a low cost MOSFET gate driver IC in multi sim and the controller is developed in lab view. A hall sensor is usually used to commutate the BLDC motor and the hall sensor output is used as the gate pulse for the six pulse inverter. The effectiveness of the design was verified through co-simulation.Keywords: BLDC, gate driver circuit, control circuit multi sim, lab view, co-simulation. I.INTRODUCTION Brushless dc motor (BLDC)are also known as synchronous motors, in which the rotor is a permanent magnet and the stator is the steel lamination stacked inside the stator slots [1]. In order to control the positioning of the rotor and the speed of the BLDC motor hall effect sensors are used.For obtaining a reliable speed, from the hall sensor signals control for time difference inverse is found. At lower speed the sampling time for speed regulation is still more. The sampling time is dependent to the motor speed and this makes it difficult to design the speed regulator for the model. For obtaining accurate speed control and to reduce any difficulty in designing speed regulator a low resolution encoders that works in low speed is used with the BLDC motor . A simple and easy implementation based on the reduced order disturbance torque at an instantaneous speed was explained by N.J Kim [2]. In [3] a new method was proposed to obtain the rotor speed and position with an out time delay known as dual observer. A low speed model with free enhancement differentiator was proposed for improving the velocity [4] [5.]. While considering the BLDC motor commonly used speed recognization method is using the back emf estimation. When the rotor speed is very less this method cannot be used to find the speed and position due to the inverter design and variation in parameters used. This paper presents study of controlling the speed of a brushless dc motor with the hall effect sensor using multisim and labview.

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The generation of power is not uniformly distributed allover the globe. The electric power is required to be shared between Nations with and without natural resources to generate power. This however needs collaborative control. It is necessary that the power demand of the sharing Nations should not cause generators to produce power beyond their capacity and also to maintain supply of power within capacity. The paper therefore presents an expert system which fulfills the objective and provides collaborative control over sharing of power, leading to economic and industrial growth of both the countries. Control of power flow through Tie-line has been exercised to meet the objective. The expert system serves to provide rule based control of sharing of powers. It follows an algorithm based control. The control is exercised through regular monitoring of power being supplied from power producing state to non power producing state. It is also being decided by the operational cost of the power generation on the other hand. Efforts have been made to keep this cost minimum using Genetic Algorithm which serves to yield the optimal cost of operation. The proposed model has been tested for real time conditions and is found to have given satisfactory practical results.