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Abstract: Recently, artificial intelligence technique is increasingly receiving attention in solving complex and practical problem and they are widely applying in electrical machine domain. The authors consider also the direct torque control (DTC) as an alternative to conventional methods of control by pulse width modulation (PWM) and by Field oriented control (FOC), so the application of the DTC based on artificial intelligence can show more advantages and simplified control algorithms with high performance. For this reason, the objectives of this paper can be divided into two parts, the first part is to apply neural networks and fuzzy logic techniques to the DTC control in the presence of a loop speed control comparing to the conventional regulators (as PI) to show the feasibility of these approaches, the second part is to further improve the performance of the neural network by using a neural-fuzzy regulator which combine the advantages of two techniques. Simulation results confirm the validity of the proposed techniques.
Key words: Direct torque control, induction motor, neural network, fuzzy, PI.
1. Introduction
High efficiency control and estimation techniques related to induction motors (IMs) have been finding more and more application with Blaschke’s well-known field oriented control (FOC) method, established in 1971.
There has been an extensive amount of work to improve the dynamic response and reduce the complexity of FOC methods. One such technique is the direct torque control (DTC) method developed by Takahashi in 1984 [1], direct torque control (DTC) of induction motor drives offers high performance in terms of simplicity in control and fast electromagnetic torque response. With dominant characteristics, the direct torque controlled induction motor drive is alternative in industrial applications.
Principle of the classical DTC is its decoupled control of stator flux and electromagnetic torque using hysteresis control of stator flux error and torque error and stator flux position. A switching look-up table is included for selection of voltage vectors feeding the induction motor [2]. DTC has developed significantly in recent years especially with the evolution of the integration of new techniques of artificial intelligence such as neural networks, fuzzy logic, genetic algorithms, etc.
Fuzzy system has been successfully applied to many control problems because it needs no accurate mathematical models of the system under control and it can cooperate with human experts’ knowledge. There exists voluminous literature on the subject of making use of various fuzzy control techniques for nonlinear systems [3]. In recent years, neural fuzzy networks have become a popular research topic. They are widely applied in fields such as time series prediction, control problem, and pattern recognition. The reason is that neural fuzzy networks combine the semantic transparency of rule-based fuzzy systems with the learning capability of neural networks [4].
In this work, the authors are interested to compare the conventional Proportional Integrator PI-type controller with a fuzzy controller (FLC) then a neuronal fuzzy controller to confirm that controllers based on artificial intelligence can provide good dynamic performance and robustness.
The paper is organized as follows: Section 2 shows the principal of DTC; section 3 discusses the traditional speed controller; sections 4 and 5 introduce the improved speed control by using fuzzy and neural approaches respectively; section 6 presents results and discussions; section 7 gives conclusions.
References
[1] Y. Kumsuwana, S. Premrudeepreechacharna, H.A. Toliyat, Modified direct torque control method for induction motor drives based on amplitude and angle control of stator flux, Electric Power Systems Research 78(2008) 1712-1718.
[2] M. Barut, S. Bogosyan, M. Gogasan, Speed sensorless direct torque control of IMs with rotor resistance estimation, Energy Conversion and Management 46 (2005) 335-349.
[3] M. Zhang, H. Zhangi, Robust adaptive fuzzy control scheme for nonlinear system with uncertainty, Journal of Control Theory and Applications 3 (2006) 209-216.
[4] M.R. Zolghadri, Contr?le directe du couple des actionneurs synchrones, Ph.D. Thesis, Superior National Polytechnic School of Grenoble, 1997.
[5] R. Toufouti, H. Benalla, S. Meziane, New direct torque neuro-fuzzy control based SVM-three level inverter-fed induction motor, International Journal of Control Automation, and System 8 (2) (2010) 425-432.
[6] P. Poure, F. Aubépart, F. Braun, A design methodology for hardware prototyping of integrated AC drive control: Application to direct torque control of an induction machine, in: Proceedings of the 11th IEEE International Workshop on Rapid System Prototyping (RSP 2000).
[7] I.E. Hassan, E.V. Westerholt, X. Roboam, B.D. Fomel, Original direct torque control strategy for speed-sensorless induction motors using extended Kalman filtering, in: Proceedings of International Conference on Power Electronic Drives and Energy Systems for Industrial Growth, 1998, pp. 32-37.
[8] Z. Lu, H. Sheng, H.L. Hess, K.M. Buck, The modeling and simulation of a permanent magnet synchronous motor with direct torque control based on Matlab/Simulink, in: IEEE International Conference on Electric Machines and Drives, Texas, 2005.
[9] Y.A. Chapuis, C. Girered, F. Aupepard, F. Braun, Quantization problem analysis on ASIC-based direct torque control of an induction machine, in: Proceedings of the 24th Annual Conference of the Industrial Electronics Society, Strasbourg, France, 1998, pp. 1527-1532.
[10] D. Casadie, G. Serra, A. Tani, The use of matrix converters in direct torque control of induction machine, IEEE Transactions on Industrial Electronics 48 (2001) 1057-1064.
[11] A. Jidin, N.R.N. Idris, H.M. Yatim, Study on stability and performances of DTC due to stator resistance variation, in: The 5th Student Conference on Research and Development (SCOReD 2007), 2007, pp. 1-6.
Key words: Direct torque control, induction motor, neural network, fuzzy, PI.
1. Introduction
High efficiency control and estimation techniques related to induction motors (IMs) have been finding more and more application with Blaschke’s well-known field oriented control (FOC) method, established in 1971.
There has been an extensive amount of work to improve the dynamic response and reduce the complexity of FOC methods. One such technique is the direct torque control (DTC) method developed by Takahashi in 1984 [1], direct torque control (DTC) of induction motor drives offers high performance in terms of simplicity in control and fast electromagnetic torque response. With dominant characteristics, the direct torque controlled induction motor drive is alternative in industrial applications.
Principle of the classical DTC is its decoupled control of stator flux and electromagnetic torque using hysteresis control of stator flux error and torque error and stator flux position. A switching look-up table is included for selection of voltage vectors feeding the induction motor [2]. DTC has developed significantly in recent years especially with the evolution of the integration of new techniques of artificial intelligence such as neural networks, fuzzy logic, genetic algorithms, etc.
Fuzzy system has been successfully applied to many control problems because it needs no accurate mathematical models of the system under control and it can cooperate with human experts’ knowledge. There exists voluminous literature on the subject of making use of various fuzzy control techniques for nonlinear systems [3]. In recent years, neural fuzzy networks have become a popular research topic. They are widely applied in fields such as time series prediction, control problem, and pattern recognition. The reason is that neural fuzzy networks combine the semantic transparency of rule-based fuzzy systems with the learning capability of neural networks [4].
In this work, the authors are interested to compare the conventional Proportional Integrator PI-type controller with a fuzzy controller (FLC) then a neuronal fuzzy controller to confirm that controllers based on artificial intelligence can provide good dynamic performance and robustness.
The paper is organized as follows: Section 2 shows the principal of DTC; section 3 discusses the traditional speed controller; sections 4 and 5 introduce the improved speed control by using fuzzy and neural approaches respectively; section 6 presents results and discussions; section 7 gives conclusions.
References
[1] Y. Kumsuwana, S. Premrudeepreechacharna, H.A. Toliyat, Modified direct torque control method for induction motor drives based on amplitude and angle control of stator flux, Electric Power Systems Research 78(2008) 1712-1718.
[2] M. Barut, S. Bogosyan, M. Gogasan, Speed sensorless direct torque control of IMs with rotor resistance estimation, Energy Conversion and Management 46 (2005) 335-349.
[3] M. Zhang, H. Zhangi, Robust adaptive fuzzy control scheme for nonlinear system with uncertainty, Journal of Control Theory and Applications 3 (2006) 209-216.
[4] M.R. Zolghadri, Contr?le directe du couple des actionneurs synchrones, Ph.D. Thesis, Superior National Polytechnic School of Grenoble, 1997.
[5] R. Toufouti, H. Benalla, S. Meziane, New direct torque neuro-fuzzy control based SVM-three level inverter-fed induction motor, International Journal of Control Automation, and System 8 (2) (2010) 425-432.
[6] P. Poure, F. Aubépart, F. Braun, A design methodology for hardware prototyping of integrated AC drive control: Application to direct torque control of an induction machine, in: Proceedings of the 11th IEEE International Workshop on Rapid System Prototyping (RSP 2000).
[7] I.E. Hassan, E.V. Westerholt, X. Roboam, B.D. Fomel, Original direct torque control strategy for speed-sensorless induction motors using extended Kalman filtering, in: Proceedings of International Conference on Power Electronic Drives and Energy Systems for Industrial Growth, 1998, pp. 32-37.
[8] Z. Lu, H. Sheng, H.L. Hess, K.M. Buck, The modeling and simulation of a permanent magnet synchronous motor with direct torque control based on Matlab/Simulink, in: IEEE International Conference on Electric Machines and Drives, Texas, 2005.
[9] Y.A. Chapuis, C. Girered, F. Aupepard, F. Braun, Quantization problem analysis on ASIC-based direct torque control of an induction machine, in: Proceedings of the 24th Annual Conference of the Industrial Electronics Society, Strasbourg, France, 1998, pp. 1527-1532.
[10] D. Casadie, G. Serra, A. Tani, The use of matrix converters in direct torque control of induction machine, IEEE Transactions on Industrial Electronics 48 (2001) 1057-1064.
[11] A. Jidin, N.R.N. Idris, H.M. Yatim, Study on stability and performances of DTC due to stator resistance variation, in: The 5th Student Conference on Research and Development (SCOReD 2007), 2007, pp. 1-6.