International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 04 | Apr -2017
p-ISSN: 2395-0072
www.irjet.net
Adaptive Sliding Mode-MRAS Strategy for Sensorless Speed Control of SPIM Drives L. Sunil1 PG Scholar, Dept. Of Electrical & Electronics Engineering, JNTUACEA, Ananthapuramu, A.P., India ---------------------------------------------------------------------***--------------------------------------------------------------------1
Abstract – The objective of this paper is to provide
sensorless control of Single Phase Induction Motor (SPIM) drives to improve the rotor speed and the stator currents. In order to achieve this high performance, adaptive Super Twisting Algorithm (STA) is used. The proposed method for the estimation of speed is based on Sliding Mode Model Reference Adaptive System (SM-MRAS) observer. An adaptive time varying switching gain is designed and adopted in order to cancel disturbance and uncertainties. To improve the estimator generated signal a discrete low pass filter is used. It represents a very simple design process compared to other chattering reduction methods as adding an observer. By using the Lyapunov approach the stability of the SM-MRAS speed estimation algorithm is proved. Simulation results prove the effectiveness of the proposed sensorless speed control algorithm.
Key Words: Adaptive Sliding Mode-MRAS, Chattering, Super twisting algorithm, Single Phase Induction Motor (SPIM), Sensor-less sliding mode control.
1. INTRODUCTION Variable Speed Drives (VSDs) applications are vastly used in the industry to control a wide range of speed and torque for machines, manufacturing process, pumps etc. VSDs have been integrated in several applications to accomplish one or more of the following objectives: energy saving, mechanical v i b r a t i o n reduction, power factor improvement, better coordination of motion on various shafts and production gains. In particular, the use of VSDs becomes recommended in many applications employing the Single Phase Induction Motors (SPIMs), such as blowers, washing machines, air conditioner, fans, compressors and pumps. Therefore, diverse control approaches have been proposed along the last years to drive the SPIM speed. Nowadays, Field-Oriented Controlled (FOC) induction motors are widely adopted to obtain high-dynamic performance in drive systems. Advanced controls such as Indirect Rotor Field- Oriented Control (IRFOC) need specific knowledge of the rotational speed information for feedback control. This information can be obtained via a mechanical sensor. Nowadays, many efforts are made to implement the sensorless control strategy to simplify the control structure and cut down the cost. In several applications different methods are applied to sensorless speed control of Induction Š 2017, IRJET
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Motor (IM) drive. The Extended Kalman Filter (EKF) has been extensively used for sensorless speed control because it has low-pass filter characteristics, and can extend the estimation of some parameters of the system and provides the best noise immunity. However, EKF is the most complicated to be implemented in DSP, instable due to linearization and erroneous parameters and its low speed performance is relatively poor. Compared to other methods, Sliding Mode Observer (SMO) method has attractive advantages of robustness to disturbances and low sensitivity to the system parameter variations. However, the chattering phenomenon limits the practical applications of conventional sliding mode observers. The Model Reference Adaptive System (MRAS) techniques are generally used for sensorless control of three-phase IM. In [7], the MRAS was compared with the EKF. It was found that the algorithm of MRAS is much simpler and faster. Due to their design simplicity, the speed estimator techniques based MRAS is widely used in the modernized industrial control. The drawback of MRAS is the large influence of parameter deviation at low- speed operation. In the other hand, this approach suffers from problems associated with pure integration, which limits the performance of the estimator at low values of stator frequency. In order to keep acceptable dynamic performances, simultaneous accurate knowledge or estimation of speed and at least one of motor parameters is required. As a result, classical MRAS algorithms become more complex and increase the installation cost which represents a severe constraint for real time implantation. Finally, in high speed, each observer can provide excellent performance but MRAS and SMO are more applicable in practical than EKF. Up to now, the speed estimation of field-oriented control SPIMs has been rarely presented so far. In fact, winding asymmetry in SPIM causes extra coupling between two stator windings and results in unbalanced machine operation. In the existing literature, some approaches have been suggested for speed sensorless SPIM, employing stator voltages and currents. In paper, the authors suggested to estimate the motor speed using rotor voltage vector which is defined in complex domain. The estimated speed is obtained from measurement only of q-axis stator current and that of reference generated by the control algorithm. Otherwise, in this method, both currents are measured and the machine parameters are required to implement the control system; therefore,
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