Self-tuning and self-adaptive PIP control systems

2011 ◽  
pp. 220-259 ◽  
Author(s):  
P. Young ◽  
M. Behzadi ◽  
A. Chotai
Keyword(s):  
Control Systems ◽  
Self Tuning ◽  
Self Adaptive

scholarly journals Sensitivity Minimisation for the Class of Self-Tuning Control Systems

1995 ◽  
Vol 28 (5) ◽  
pp. 181-187
Author(s):  
Andrzej Tarczynski
Keyword(s):  
Control Systems ◽  
Self Tuning

Application of Active Mount Engine Vibration Isolation to Fuzzy Control Systems

2013 ◽  
Vol 341-342 ◽  
pp. 1023-1027
Author(s):  
Hui Guo ◽  
Guo Chun Sun ◽  
Min Fan
Keyword(s):  
Control Systems ◽  
Pid Control ◽  
Vibration Isolation ◽  
The Body ◽  
Fuzzy Pid Control ◽  
Power Train ◽  
Engine Vibration ◽  
Control Scheme ◽  
Self Tuning ◽  
Piezoelectric Stack Actuator

An automobile power-train active mount system with a piezoelectric stack actuator is introduced. The influence caused by power-train to the body of the car is analyzed by means of parameter self-tuning fuzzy PID control, on which the simulating results are based. It turns out that this control scheme can restrain the influence better caused by power-train to the body of the car.

scholarly journals Neural Networks for Self-tuning Control Systems

10.14311/514 ◽  
2004 ◽  
Vol 44 (1) ◽  
Author(s):  
A. Noriega Ponce ◽  
A. Aguado Behar ◽  
A. Ordaz Hernández ◽  
V. Rauch Sitar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Control Systems ◽  
Control Algorithm ◽  
Set Point ◽  
Tuning Mechanism ◽  
Self Tuning ◽  
The Moment ◽  
Weighting Coefficients ◽  
Previous Training

In this paper, we presented a self-tuning control algorithm based on a three layers perceptron type neural network. The proposed algorithm is advantageous in the sense that practically a previous training of the net is not required and some changes in the set-point are generally enough to adjust the learning coefficient. Optionally, it is possible to introduce a self-tuning mechanism of the learning coefficient although by the moment it is not possible to give final conclusions about this possibility. The proposed algorithm has the special feature that the regulation error instead of the net output error is retropropagated for the weighting coefficients modifications. 

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