A Novel Hybrid Recurrent Wavelet Neural Network Control for a PMSM Driven Electric Scooter

2013 ◽  
Vol 284-287 ◽  
pp. 2194-2198 ◽  
Author(s):  
Chih Hong Lin ◽  
Chih Peng Lin
Keyword(s):  
Neural Network ◽  
Control System ◽  
Permanent Magnet Synchronous Motor ◽  
Wavelet Neural Network ◽  
Network Control ◽  
Learning Ability ◽  
Electric Scooter ◽  
Supervisor Control ◽  
On Line ◽  
On Line Learning

The electric scooter with nonlinear friction force of the transmission belt made the hybrid recurrent neural network (HRNN) control system with degenerated tracking responses. In order to overcome this problem, a hybrid recurrent wavelet neural network (HRWNN) control system is proposed to control for a permanent magnet synchronous motor (PMSM) driven electric scooter. The HRWNN control system consists of a supervisor control, a RWNN and a compensated control with adaptive law. The on-line parameter training methodology of the RWNN can be derived using adaptation laws and the Lyapunov stability theorem. The RWNN has the on-line learning ability to respond to the system’s nonlinear and time-varying behaviors. To show the effectiveness of the proposed controller, comparative studies with HRNN control system is demonstrated by experimental results.

Novel Adaptive Recurrent Legendre Neural Network Control for PMSM Servo-Drive Electric Scooter

2014 ◽  
Vol 137 (1) ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Control System ◽  
Permanent Magnet Synchronous Motor ◽  
Parameter Tuning ◽  
Network Control ◽  
The Novel ◽  
Servo Drive ◽  
Control Scheme ◽  
Electric Scooter ◽  
Adaptation Law

Because an electric scooter driven by permanent magnet synchronous motor (PMSM) servo system has the unknown nonlinearity and the time-varying characteristics, its accurate dynamic model is difficult to establish for the design of the linear controller in whole system. In order to conquer this difficulty and raise robustness, a novel adaptive recurrent Legendre neural network (NN) control system, which has fast convergence and provide high accuracy, is proposed to control for PMSM servo-drive electric scooter under external torque disturbance in this study. The novel adaptive recurrent Legendre NN control system consists of a recurrent Legendre NN control with adaptation law and a remunerated control with estimation law. In addition, the online parameter tuning methodology of the recurrent Legendre NN control and the estimation law of the remunerated control can be derived by using the Lyapunov stability theorem. Finally, comparative studies are demonstrated by experimental results in order to show the effectiveness of the proposed control scheme.

Research on Neural Network Control Technology for Satellite-Based Laser Communication Tracking Turntable

2014 ◽  
Vol 945-949 ◽  
pp. 2266-2271
Author(s):  
Li Hua Wang ◽  
Xiao Qiang Wu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Mathematic Model ◽  
Network Control ◽  
Learning Ability ◽  
Space Environment ◽  
Laser Communication ◽  
Self Learning

In space laser communication tracking turntable work environment characteristics, we design a neural network PID control system which makes the system’s parameter self-tuning. The control system cans self-tune parameters under the changes of the object’ mathematic model, it solves the problem for the control object’s model changes under the space environment. It also looks for method for optimum control through the function of neural network's self-learning in order to solve the problem of the precision’s decline which arouse from vibration and disturbance. The simulation experiments confirmed the self-learning ability of neural network, and described the neural PID controller dynamic performance is superior to the classical PID controller through the output characteristic curves contrast.

Sign in / Sign up

Export Citation Format

Share Document