Hybrid Neural Network and Genetic Algorithms for Self-tuning of PI Controller in DSPM Motor Drive System

2006 ◽  
pp. 1068-1074
Author(s):  
Rui-Ming Fang ◽  
Qian Sun
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Pi Controller ◽  
Drive System ◽  
Motor Drive ◽  
Hybrid Neural Network ◽  
Self Tuning ◽  
Motor Drive System

New Neural Network Inverse Control of Two-Motor Drive System

2013 ◽  
Vol 416-417 ◽  
pp. 447-453
Author(s):  
Mei Kang ◽  
Wen Xiang Zhao ◽  
Jing Hua Ji ◽  
Guo Hai Liu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Motor System ◽  
Rbf Neural Network ◽  
Particle Swarm ◽  
Drive System ◽  
Motor Drive ◽  
Swarm Optimization ◽  
Synchronous System ◽  
Motor Drive System

Two-motor drive system is a multi-variable, nonlinear and strongly coupled system. A new synchronous control strategy for two-motor system is proposed based on radial basis function (RBF) neural network inverse with particle swarm optimization. To enhance the system performance, the particle swarm optimization is adopted to optimize the RBF nerve center, an optimized RBF neural network inverse and a two-motor system is connected in series to form composite pseudo-linear system. This two-motor synchronous system can be decoupled into two independent linear subsystems for speed and tension. Then, the decoupled control is implemented by designing a linear closed-loop adjustor. The experimental results verify that the two-motor synchronous system can be decoupled well for speed and tension based on the proposed neural network inverse system. Also, the proposed system can deal with external disturbances with strong robustness.

scholarly journals A Rectified Reiterative Sieved-Pollaczek Polynomials Neural Network Backstepping Control with Improved Fish School Search for Motor Drive System

Mathematics ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1699
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Backstepping Control ◽  
Drive System ◽  
Motor Drive ◽  
Switching Function ◽  
Fish School ◽  
Squirrel Cage ◽  
Pollaczek Polynomials ◽  
Motor Drive System

As the six-phase squirrel cage copper rotor induction motor has some nonlinear characteristics, such as nonlinear friction, nonsymmetric torque, wind stray torque, external load torque, and time-varying uncertainties, better control performances cannot be achieved by utilizing general linear controllers. The snug backstepping control with sliding switching function for controlling the motion of a six-phase squirrel cage copper rotor induction motor drive system is proposed to reduce nonlinear uncertainty effects. However, the previously proposed control results in high chattering on nonlinear system effects and overtorque on matched uncertainties. So as to reduce the immense chattering situation, we then put forward the rectified reiterative sieved-Pollaczek polynomials neural network backstepping control with an improved fish school search method to estimate the external bundled torque uncertainties and to recoup the smallest reorganized error of the evaluated rule. In the light of Lyapunov stability, the online parametric training method of the rectified reiterative sieved-Pollaczek polynomials neural network can be derived by utilizing an adaptive rule. Moreover, to improve convergence and obtain beneficial learning manifestation, the improved fish school search algorithm is made use of to readjust two fickle learning rates of the weights in the rectified reiterative sieved-Pollaczek polynomials neural network. Lastly, the effectuality of the proposed control system is validated by examination results.

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