Sliding Mode Neuro-Adaptive Control of Electric Drives

An innovative variable-structure-systems-based approach for online training of neural network (NN) controllers as applied to the speed control of electric drives is presented. The proposed learning algorithm establishes an inner sliding motion in terms of the controller parameters, leading the command error towards zero. The outer sliding motion concerns the controlled electric drive, the state tracking error vector of which is simultaneously forced towards the origin of the phase space. The equivalence between the two sliding motions is demonstrated. In order to evaluate the performance of the proposed control scheme and its practical feasibility in industrial settings, experimental tests have been carried out with electric motor drives. Crucial problems such as adaptability, computational costs, and robustness are discussed. Experimental results illustrate that the proposed NN-based speed controller possesses a remarkable learning capability to control electric drives, virtually without requiring a priori knowledge of the plant dynamics and laborious startup procedures

[1]  Okyay Kaynak,et al.  Online learning in adaptive neurocontrol schemes with a sliding mode algorithm , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[2]  Xinghuo Yu,et al.  Sliding Mode Control of a Three Degrees of Freedom Anthropoid Robot by Driving the Controller Parameters to an Equivalent Regime , 2000 .

[3]  A. Chu,et al.  A novel neural network controller and its efficient DSP implementation for vector controlled induction motor drives , 2002 .

[4]  Okyay Kaynak,et al.  Sliding Mode Algorithm for Online Learning in Analog Multilayer Feedforward Neural Networks , 2003, ICANN.

[5]  T. C. Chen,et al.  Model reference neural network controller for induction motor speed control , 2002 .

[6]  Zhihong Man,et al.  Adaptive sliding mode approach for learning in a feedforward neural network , 2005, Neural Computing & Applications.

[7]  Ahmed Rubaai,et al.  Development and implementation of an adaptive fuzzy-neural-network controller for brushless drives , 2000, Conference Record of the 2000 IEEE Industry Applications Conference. Thirty-Fifth IAS Annual Meeting and World Conference on Industrial Applications of Electrical Energy (Cat. No.00CH37129).

[8]  Okyay Kaynak,et al.  Stabilizing and robustifying the learning mechanisms of artificial neural networks in control engineering applications , 2000 .

[9]  R. Krishnan,et al.  Electric Motor Drives: Modeling, Analysis, and Control , 2001 .

[10]  Okyay Kaynak,et al.  Neural network modeling and control of cement mills using a variable structure systems theory based on-line learning mechanism , 2004 .

[11]  D. M. Vilathgamuwa,et al.  Implementation of an artificial-neural-network-based real-time adaptive controller for an interior permanent-magnet motor drive , 2003 .

[12]  Okyay Kaynak,et al.  Neural Network Closed-Loop Control Using Sliding Mode Feedback-Error-Learning , 2004, ICONIP.

[13]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[14]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[15]  Antônio de Pádua Braga,et al.  Sliding mode algorithm for training multilayer artificial neural networks , 1998 .

[16]  Werner Leonhard,et al.  Control of Electrical Drives , 1990 .

[17]  Okyay Kaynak,et al.  Stable training of computationally intelligent systems by using variable structure systems technique , 2000, IEEE Trans. Ind. Electron..

[18]  Eliezer Colina-Morles,et al.  A sliding mode strategy for adaptive learning in Adalines , 1995 .

[19]  Ahmed Rubaai,et al.  A continually online-trained neural network controller for brushless DC motor drives , 2000 .

[20]  Hung-Yuan Chung,et al.  Neuro-sliding mode control with its applications to seesaw systems , 2001, IEEE Transactions on Neural Networks.