In consideration of the difficulty to install speed sensor result from special high temperature working environment of submersible motor, in this paper, a method of sliding mode model reference adaptive observer(SMMRAS) is used to estimate the speed of sensor less vector controlled submersible motor. This method combines variable structure control with model reference adaptive system (MRAS) to improve the accuracy of speed identification, and the stability and speediness capability of the system are proved by Lyapunov theory. The model of the speed-sensor less vector control system of induction motor is built by MatLab/Simulink. Theoretical analysis and the MATLAB simulation results show that the proposed method used in the system for speed identification has rapid response, and the static and dynamic performance is also perfect

1. ELECTRICAL PROJECTS USING MATLAB/SIMULINK
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Sliding Mode MRAS Speed Sensor less Vector
Control for Submersible Motor
ABSTRACT:
In consideration of the difficulty to install speed sensor result from special high temperature
working environment of submersible motor, in this paper, a method of sliding mode model
reference adaptive observer(SMMRAS) is used to estimate the speed of sensor less vector
controlled submersible motor. This method combines variable structure control with model
reference adaptive system (MRAS) to improve the accuracy of speed identification, and the
stability and speediness capability of the system are proved by Lyapunov theory. The model of
the speed-sensor less vector control system of induction motor is built by MatLab/Simulink.
Theoretical analysis and the MATLAB simulation results show that the proposed method used in
the system for speed identification has rapid response, and the static and dynamic performance is
also perfect
.
KEYWORDS:
1. Submersible motor
2. Speed sensor less
3. Model reference adaptive system
4. MRAS
5. Speed estimation
SOFTWARE: MATLAB/SIMULINK

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CONTROL SYSTEM:
Fig. 1. Speed identification scheme based upon MRAS
Fig. 2. Speed identification scheme based upon SM MRAS

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EXPECTED SIMULATION RESULTS:
a) Actual speed and estimated speed b) Torque response
Fig. 3Speed and torque curve when load increasing
(a) Actual speed and estimated speed (b) Torque response
Fig4. Speed and torque curve of starting and braking

4. ELECTRICAL PROJECTS USING MATLAB/SIMULINK
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0-9347143789/9949240245
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CONCLUSION:
In this paper the sliding mode speed observer is established. Stability conditions of a model
convergence is introduced by the Lyapunov stability theory. Use the space vector pulse width
modulation (SVPWM) technology make the voltage control signal of motor is better
optimization. Siding mode speed observer is to reduce the influence of parameters on the system,
and to improve the accuracy of the speed identification. In this paper the method can better to
achieve the speed identification of motor, has robustness to the parameter changes, can quickly
follow the actual rational speed changes. Simulation results were given in the transient and
steady states for various operating condition. The simulation results verify that the proposed
control schemes provide good dynamics performance in tracking accuracy and disturbance
rejection
REFERENCES:
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induction motor drive based on recurrent fuzzy neural network[J]. Proceedings of the
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[2] Su W F,Liu C W,Sun X D,et al.Speed controller for induction motors based on kalman
filtering[J].Journal of Tsinghua University(Science and Technology),2003,43(9): 1202-1205(in
Chinese)
.[3] Zhang P F, Peng W D, Liu X G. Control of Induction Motor Based on
Model Reference Adaptive System[J].2011,34(1):197-199.
[4] Deng H, Xue B, Xu D G, Yang Jing. Speed Estimation for Submersible Motor Based on
Elman Neural Network[J]. Proceedings of the CSEE,2007,27(24):102-106(in Chinese).
[5] Schauder C. Adaptive speed identification for vector control of induction motors without
rotational transducers[J].IEEE Transactions on Industry Applications,1992,28(5):1054-1061.