Novel design of a Takagi-Sugeno fuzzy strategy for induction motor speed control

This paper presents a novel design of a Takagi-Sugeno fuzzy logic control scheme for controlling some of the parameters, such as speed, torque, flux, voltage, current, etc. of the induction motor. Induction motors are characterized by highly non-linear, complex and time-varying dynamics and inaccessibility of some of the states and outputs for measurements and hence it can be considered as a challenging engineering problem. The development of advanced control techniques has partially solved some of the induction motor's speed control problems; because they were sensitive to drive parameter variations and the performance may deteriorate if conventional controllers are used. Fuzzy logic based controllers are considered as potential candidates for such an application. Further, the Takagi- Sugeno control strategy coupled with fuzzy logic rule based approach when employed to the induction motor yields excellent results compared to the other methods as this becomes a hybrid & integrated method of approach. Such a mixed implementation leads to a more effective control design with improved system performance, cost-effectiveness, efficiency, dynamism, reliability & robustness. Due to the usage of the TS-FLC concept in closed loop with the plant, the dynamic characteristics of the AC drives increases as the developed strategy does not require the mathematical model of the controller unlike that of the conventional electrical drive controller, which uses the mathematical model, which is the highlight of the paper. The sudden fluctuation or change in speed & its effect on the various parameters of the dynamic system is also considered in this paper. The designed controller not only takes care of the sudden perturbations in load torque & speed, but also brings back the parameters to the reference or the set value in fraction of seconds, thus exhibiting the robustness behavior. In other sense, the designed controller is robust to parametric variations. The closed loop speed control of the induction motor using the above technique thus provides a reasonable degree of accuracy which can be observed from the simulation results depicted at the end. Simulink based block model of induction motor drive was developed & used for the simulation purposes. Further, its performance is thereby evaluated for the control of various parameters. The method presented in this paper provides robustness of the induction machine towards the parametric variations compared to the conventional speed control of induction motor drives & has got a faster response time or settling times. The simulation results presented in this paper show the effectiveness of the method developed & have got a wide number of advantages in the industrial sector & can be converted into a real time application using some interfacing cards.

[1]  Chuen-Chien Lee FUZZY LOGIC CONTROL SYSTEMS: FUZZY LOGIC CONTROLLER - PART I , 1990 .

[2]  R. Arulmozhiyal,et al.  Space Vector Pulse Width Modulation BasedSpeed Control of Induction Motor using FuzzyPI Controller , 2009 .

[3]  M. Sugeno,et al.  Structure identification of fuzzy model , 1988 .

[4]  Gang Feng,et al.  Analysis and design of fuzzy control systems using dynamic fuzzy-state space models , 1999, IEEE Trans. Fuzzy Syst..

[5]  Yu Zhang,et al.  Indirect field-oriented control of induction machines based on synergetic control theory , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[6]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[7]  Luis Canali,et al.  Scalar Speed Control of a dq Induction Motor Model Using Fuzzy Logic Controller , 2006 .

[8]  Peter Vas,et al.  Sensorless vector and direct torque control , 1998 .

[9]  Thierry Marie Guerra,et al.  Robust Takagi–Sugeno fuzzy control of a spark ignition engine , 2007 .

[10]  Adriano Carvalho,et al.  Technological trends in induction motor electrical drives , 2001, 2001 IEEE Porto Power Tech Proceedings (Cat. No.01EX502).

[11]  Xiaohong Zhang,et al.  Impulsive stability of chaotic systems represented by T-S model , 2009 .

[12]  D. S. Zinger,et al.  Fuzzy controller for inverter fed induction machines , 1992, Conference Record of the 1992 IEEE Industry Applications Society Annual Meeting.

[13]  Bimal K. Bose,et al.  Modern Power Electronics and AC Drives , 2001 .

[14]  L. Ben-Brahim Improvement of the stability of the V/f controlled induction motor drive systems , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[15]  Bo Wahlberg,et al.  Stabilization of Induction Motor Drives With Poorly Damped Input Filters , 2007, IEEE Transactions on Industrial Electronics.

[16]  Masoud Shafiee,et al.  Fuzzy affine impulsive controller , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[17]  Ashok Kusagur,et al.  AI based design of a fuzzy logic scheme for speed control of induction motors using SVPWM technique , 2009 .

[18]  Mahmoud Moghavvemi,et al.  Fuzzy-SMC-PI Flux and Speed Control for Induction Motors , 2008, 2008 IEEE Conference on Robotics, Automation and Mechatronics.

[19]  S. Sivanandam,et al.  Introduction to Fuzzy Logic using MATLAB , 2006 .

[20]  Chuntao Man,et al.  A T-S type of rough fuzzy controller based on process input-output data , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[21]  A. Trzynadlowski The Field Orientation Principle in Control of Induction Motors , 1993 .

[22]  Francis J. Doyle,et al.  Multivariable nonlinear control applications for a high purity distillation column using a recurrent dynamic neuron model , 1997 .

[23]  Leopoldo García Franquelo,et al.  Speed control of induction motors using a novel fuzzy sliding-mode structure , 2002, IEEE Trans. Fuzzy Syst..

[24]  S. Zhong,et al.  T-S fuzzy model-based impulsive control of chaotic systems with exponential decay rate , 2007 .

[25]  Allouche Moez,et al.  Takagi-Sugeno Fuzzy Control of Induction Motor , 2010 .

[26]  Toly Chen,et al.  A fuzzy sliding mode controller for induction motor position control , 1994, Proceedings of IECON'94 - 20th Annual Conference of IEEE Industrial Electronics.

[27]  Kazuo Tanaka,et al.  Successive identification of a fuzzy model and its applications to prediction of a complex system , 1991 .

[28]  Ernesto Araujo Improved Takagi-Sugeno fuzzy approach , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[29]  Chung-Yuen Won,et al.  Induction motor servo system using variable structure control with fuzzy sliding surface , 1996, Proceedings of the 1996 IEEE IECON. 22nd International Conference on Industrial Electronics, Control, and Instrumentation.

[30]  Ching-Chang Wong,et al.  Implementation of the Takagi-Sugeno model-based fuzzy control using an adaptive gain controller , 2000 .

[31]  Zdenko Kovacic,et al.  Fuzzy Controller Design: Theory and Applications , 2005 .

[32]  Chieh-Li Chen,et al.  Optimal design of fuzzy sliding-mode control: A comparative study , 1998, Fuzzy Sets Syst..

[33]  P. Vas Vector control of AC machines , 1990 .

[34]  Tsau Young Lin,et al.  The Takagi-Sugeno Fuzzy Model Identification Method of Parameter Varying Systems , 1998, Rough Sets and Current Trends in Computing.

[35]  Kazuo Tanaka,et al.  Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach , 2008 .

[36]  Rached Dhaouadi,et al.  A fuzzy learning - Sliding mode controller for direct field-oriented induction machines , 2008, Neurocomputing.

[37]  Ali Yazdian Varjani,et al.  Using Fuzzy Controller in Induction Motor Speed Control with Constant Flux , 2007, WEC.

[38]  Mouloud Denai,et al.  Fuzzy and Neural Control of an Induction Motor , 2002 .