An improved method for synchronous control of complex multi-motor system

A general mathematic model of multi-motor system working on vector control mode is given. Here, a three-motor synchronous system was taken as the research object, and was proved to be invertible. A method of generalized growing and pruning RBF (GGAP-RBF) neural network inverse for synchronous control of multi-motor system is proposed. The inverse can be constructed by combining the RBF neural network with an integrator, the speed and tension control of three-motor system can be decoupled by combing RBF neural network inverse with the three-motor system. Then, through growing and pruning RBF neural network according to the performance index given, the three-motor variable frequency speed-regulating control system is optimized, solving the problem which can't be done by BP neural network. the method provides a theoretical basis for the research of complex multi-motor synchronous system. The simulation results illustrates it good dynamic and static operation performance.

[1]  Guohai Liu,et al.  Neural Network Inverse Synchronous Control of Two-motor Variable Frequency Speed-Regulating System , 2006, 2006 IEEE International Conference on Networking, Sensing and Control.

[2]  Guohai Liu,et al.  Neural network inverse control of variable frequency speed-regulating system in V/F mode , 2005, 31st Annual Conference of IEEE Industrial Electronics Society, 2005. IECON 2005..

[3]  Dai Xian.  Neural Network Inverse Synchronous Control of Two-motor Variable Frequency Speed-regulating System , 2005 .

[4]  Gabriel Abba,et al.  Modeling and robust control of winding systems for elastic webs , 2002, IEEE Trans. Control. Syst. Technol..

[5]  C. C. Chan,et al.  The state of the art of electric and hybrid vehicles , 2002, Proc. IEEE.

[6]  Kang Mei.  Neural Network Generalized Inverse Decoupling Control of Two-motor Variable Frequency Speed-regulating System , 2008 .

[7]  G. Clark,et al.  Reference , 2008 .

[8]  D. Knittel,et al.  Tension control for winding systems with two degrees of freedom H/sub /spl infin// controller , 2001, Conference Record of the 2001 IEEE Industry Applications Conference. 36th IAS Annual Meeting (Cat. No.01CH37248).

[9]  Hideki Asada,et al.  Adaptive and Robust Control Method with Estimation of Rolling Characteristics for Looper Angle Control at Hot Strip Mill , 2003 .

[10]  Yoichi Hori.  Future vehicle driven by electricity and control-research on four wheel motored "UOT Electric March II" , 2002, 7th International Workshop on Advanced Motion Control. Proceedings (Cat. No.02TH8623).

[11]  Edouard Laroche,et al.  Tension control for winding systems with two degrees of freedom H/sub ∞/ controller , 2001 .

[12]  Farrokh Janabi-Sharifi.  A neuro-fuzzy system for looper tension control in rolling mills , 2005 .

[13]  Cheng Jin.  Design of fuzzy neural network controller for synchronization drive in multi-motor systems , 2007 .

[14]  Narasimhan Sundararajan,et al.  A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation , 2005, IEEE Transactions on Neural Networks.