Optimization and control of a small-angle negative ion source using an on-line adaptive controller based on the connectionist normalized local spline neural network

Author(s):  
W.C. Mead ◽  
P.S. Bowling ◽  
S.K. Brown ◽  
R.D. Jones ◽  
C.W. Barnes ◽  
...  
2017 ◽  
Author(s):  
G. Serianni ◽  
C. Baltador ◽  
P. Barbato ◽  
L. Baseggio ◽  
R. Cavazzana ◽  
...  

2013 ◽  
Author(s):  
Jigensh Soni ◽  
R. K. Yadav ◽  
A. Patel ◽  
A. Gahlaut ◽  
H. Mistry ◽  
...  

2000 ◽  
Vol 123 (2) ◽  
pp. 253-264
Author(s):  
Rong-Fong Fung ◽  
Faa-Jeng Lin ◽  
Rong-Jong Wai

The dynamic response of an adaptive fuzzy neural network (FNN) controlled quick-return mechanism, which is driven by a permanent magnet (PM) synchronous servo motor, is described in this study. The crank and disk of the quick-return mechanism are assumed to be rigid. First, Hamilton’s principle and Lagrange multiplier method are applied to formulate the mathematical model of motion. Then, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the quick-return servomechanism. Moreover, since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the quick-return servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Simulated and experimental results due to periodic step and sinusoidal commands show that the dynamic behavior of the proposed adaptive FNN control system are robust with regard to parametric variations and external disturbances.


1993 ◽  
Vol 3 (1) ◽  
pp. 3-15 ◽  
Author(s):  
Garry A. Labossiere ◽  
Peter L. Lee

1989 ◽  
Vol 111 (2) ◽  
pp. 133-139 ◽  
Author(s):  
S. D. Fassois ◽  
K. F. Eman ◽  
S. M. Wu

A fast, on-line algorithm for machining process modeling and control is proposed. The modeling is accomplished via a new recursive estimator that offers good accuracy at a minimal computational load. Its Fast Kalman-type version, that further reduces its computational complexity, is also presented. The adaptive controller, which is based on on-line identification and closed-loop pole assignment, is characterized by a low computational load and no need for a priori process information. The analytical results are supplemented by numerical simulations, where the proposed scheme is used for the control of a turning operation and shown to offer very good performance under noisy conditions and suddenly changing machining dynamics.


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