Speed Sensorless Vector Control of Induction Motor Drive with PI and Fuzzy Controller

Abstract

This paper directed the speed-sensorless vector control of induction motor drive with PI and fuzzy controllers. Natural observer with fourth order state space model is employed to estimate the speed and rotor fluxes of the induction motor. The formation of the natural observer is similar to and as well as its attribute is identical to the induction motor. Load torque adaptation is provided to estimate the torque and rotor speed is estimated from the load torque, rotor fluxes and stator currents. There is no direct feedback in natural observer and also observer gain matrix is absent. Both the induction motor and the observer are characterized by state space model. Simple fuzzy logic controller and conventional PI controllers are used to control the speed of the induction motor in closed loop. MATLAB simulations are made with PI and fuzzy controllers and the performance of fuzzy controller is better than PI controller in view of torque ripples. The simulation results are obtained for various running conditions to exhibit the suitability of this method for sensorless vector control. Experimental results are provided for natual observer based sensorless vector control with conventional PI controller. Keyword:Fuzzy INTRODUCTIONInduction motors are preferred for most of the industry applications because of the limitations of commutation and rotor speed in DC drives. The induction motor is in fact 'brushless' and can operate with simple control methods not requiring a shaft position transducer. With no shaft position feedback, the motor remains stable only as long as the load torque does not exceed the breakdown torque. At low speeds it is possible for oscillatory instabilities to develop. To overcome these limitations 'field-oriented' or 'vector' control has been developed in which the phase and magnitude of the stator currents are regulated so as to maintain the optimum angle between stator mmf and rotor flux. This control is based on transforming a three phase time and frequency dependent system into a two co-ordinate (d and q axes) time invariant system. These projections lead to a structure similar to that of a separately excited DC motor control. Field orientation, however, requires either a shaft position encoder or an in-built control model whose parameters are specific to the motor.Generally, two types of field oriented control schemes are available. 1. Direct field oriented control 2. Indirect field oriented control. In the direct scheme, the instantaneous position of rotor flux (θ e ) has to be measured using flux sensors. This adds to the cost and complexity of the drive system. In the indirect scheme, a model of the induction motor is required to calculate the reference angular slip frequency that has to be added to the measured rotor speed. The sum is integrated to calculate the instantaneous position of the

The sensorless control system for controlling the speed of direct current motor

“…The Direct Current (DC) motor is a traditional electric motor. In comparison to the other electric motor such as brushless DC motors [1][2][3], induction motor [4][5][6], the intrinsic structure of the DC motor [7] has advantages such as the ease of maintenance, the simple control structure, the large electromagnetic torque, and the wide range of speed control. Therefore, DC motors are increasingly widely used in areas that require high-quality control, such as crane, elevator, conveyor, steel rolling, transportation, mining, etc [8], [9][10][11][12][13].…”

An Improved of Inputs Scaling Factor Tuning Method Fuzzy Logic Speed Controller for Induction Motor Drive

“…Some of the previous researchers also applied different range of UoD for the speed error such as more than [-1,1] of UoD [12][13][14][15]. However, the discussions of the selected range and the input variable or fuzzy subsets distribution are not discovered.…”

Different Control Schemes for Sensor Less Vector Control of Induction Motor

“…The Kalman filter controller uses numerical analysis based on predictor-corrector approximation [5] and PI controller based on linearity concept for unique operating points which resulted in attaining high performance was discussed [6] by several authors. These methods are sensitive to rotor time and correct flux estimationis required.Sensor less Vector control gains its significance where induction motor can modelled similar to D.C Motor using Field orientation control where torque and flux are independently controlled.With the rapid development of power electronics and digital computation the errors in the above methods are nullified with adaptation of artificial intelligence methods,like Fuzzy controller,Artifical neural networks and Genetic Algorithms .Many authors created a control signal employing fuzzy systems [7], these controllers relay on firing rules which are framed by past experiences.…”