Adaptive fuzzy control for field-oriented induction motor drives

With the field-oriented method, the dynamic behavior of the induction motor is rather similar to that of a separately excited DC motor. However, the control performance of the induction motor is still influenced by the unmodelled dynamics or external disturbances, and to compensate for these uncertainties, adaptive fuzzy control is proposed. The overall control signal consists of two elements, (1) the equivalent control which is used for linearization of the induction motor’s model through feedback of the state vector. The equivalent control includes neurofuzzy approximators of the unknown parts of the induction motor model (2) the supervisory control which consists of an $$H_{\infty}$$ term and compensates for parametric uncertainties of the induction motor model and external disturbances. The performance of the proposed adaptive fuzzy $$H_{\infty}$$ controller is compared to backstepping nonlinear control through simulation tests.

[1]  Tao Zhang,et al.  Adaptive neural network control of nonlinear systems by state and output feedback , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[2]  Carlos Canudas de Wit,et al.  Nonlinear H 2 and H ∞ optimal controllers for current-fed induction motors , 1997 .

[3]  Gerasimos Rigatos,et al.  Adaptive fuzzy control of DC motors using state and output feedback , 2009 .

[4]  R. Marino,et al.  Adaptive input-output linearizing control of induction motors , 1993, IEEE Trans. Autom. Control..

[5]  Chun-Yi Su,et al.  Adaptive control of a class of nonlinear systems with fuzzy logic , 1994, Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference.

[6]  Rong-Jong Wai,et al.  Decoupled stator-flux-oriented induction motor drive with fuzzy neural network uncertainty observer , 2000, IEEE Trans. Ind. Electron..

[7]  Joachim Holtz,et al.  Sensorless control of induction motor drives , 2002, Proc. IEEE.

[8]  H. Yousef,et al.  Adaptive Fuzzy Decentralized Control for a Class of Large-Scale Nonlinear Systems with MIMO Subsystems , 2006, 2006 International Conference on Computer Engineering and Systems.

[9]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[10]  John Chiasson.  A new approach to dynamic feedback linearization control of an induction motor , 1998 .

[11]  Gerasimos G. Rigatos,et al.  Fuzzy stochastic automata for intelligent vehicle control , 2003, IEEE Trans. Ind. Electron..

[12]  Jui-Hong Horng,et al.  Neural Adaptive Tracking Control of a DC Motor , 1999, Inf. Sci..

[13]  Gerasimos Rigatos,et al.  Adaptive fuzzy control for the ship steering problem , 2006 .

[14]  Salim Labiod,et al.  Adaptive fuzzy control of a class of MIMO nonlinear systems , 2005, Fuzzy Sets Syst..

[15]  Spyros G. Tzafestas.  Applied Control: Current Trends and Modern Methodologies , 1993 .

[16]  Rong-Jong Wai,et al.  Implementation of robust wavelet-neural-network sliding-mode control for induction servo motor drive , 2003, IEEE Trans. Ind. Electron..

[17]  Yih-Guang Leu,et al.  Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[18]  P. Khargonekar,et al.  STATESPACE SOLUTIONS TO STANDARD 2 H AND H? CONTROL PROBLEMS , 1989 .

[19]  Bor-Sen Chen,et al.  H∞ tracking design of uncertain nonlinear SISO systems: adaptive fuzzy approach , 1996, IEEE Trans. Fuzzy Syst..

[20]  Shuzhi Sam Ge,et al.  Adaptive Fuzzy Control of a Class of Nonlinear Systems by Fuzzy Approximation Approach , 2012, IEEE Transactions on Fuzzy Systems.

[21]  Hazem N. Nounou,et al.  Application of adaptive fuzzy control to ac machines , 2007, Appl. Soft Comput..

[22]  Riccardo Marino,et al.  Global adaptive output feedback control of induction motors with uncertain rotor resistance , 1996, Proceedings of 35th IEEE Conference on Decision and Control.

[23]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[24]  Spyros G. Tzafestas,et al.  H α tracking of uncertain SISO nonlinear systems: an observer-based adaptive fuzzy approach , 2007, Int. J. Syst. Sci..

[25]  M. Bodson,et al.  High-performance induction motor control via input-output linearization , 1994, IEEE Control Systems.

[26]  Rong-Jong Wai,et al.  Backstepping wavelet neural network control for indirect field-oriented induction motor drive , 2004, IEEE Trans. Neural Networks.

[27]  Kevin M. Passino,et al.  Stable adaptive control using fuzzy systems and neural networks , 1996, IEEE Trans. Fuzzy Syst..

[28]  M. Athans,et al.  An experimental comparison of \mathcal{H}_2 and \mathcal{H}_\infty designs for an interferometer testbed , 1995 .

[29]  P. Khargonekar,et al.  State-space solutions to standard H/sub 2/ and H/sub infinity / control problems , 1989 .

[30]  Carlos Canudas de Wit,et al.  Nonlinear H2 and H∞ optimal controllers for current-fed induction motors , 1997, 1997 European Control Conference (ECC).

[31]  Spyros G. Tzafestas,et al.  Mobile robot motion control in partially unknown environments using a sliding-mode fuzzy-logic controller , 2000, Robotics Auton. Syst..

[32]  Mietek A. Brdys,et al.  Dynamic neural controllers for induction motor , 1999, IEEE Trans. Neural Networks.