Robustness Design of Fuzzy Control for Nonlinear Multiple Time-Delay Large-Scale Systems via Neural-Network-Based Approach

2008 ◽  
Vol 38 (1) ◽  
pp. 244-251 ◽  
Author(s):  
Feng-Hsiag Hsiao ◽  
Sheng-Dong Xu ◽  
Chia-Yen Lin ◽  
Zhi-Ren Tsai
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Fuzzy Control ◽  
Large Scale ◽  
Multiple Time ◽  
Large Scale Systems

Robust stabilization of nonlinear multiple time-delay large-scale systems via decentralized fuzzy control

2005 ◽  
Vol 13 (1) ◽  
pp. 152-163 ◽  
Author(s):  
Feng-Hsiag Hsiao ◽  
Jung-Dong Hwang ◽  
Cheng-Wu Chen ◽  
Zhi-Ren Tsai
Keyword(s):  
Time Delay ◽  
Fuzzy Control ◽  
Large Scale ◽  
Robust Stabilization ◽  
Multiple Time ◽  
Large Scale Systems

scholarly journals Controller Design of Multiinput Multioutput Time-Delay Large-Scale System

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Chia-Wei Lin ◽  
Tzuu-Hseng S. Li ◽  
Chung-Cheng Chen
Keyword(s):  
Time Delay ◽  
Feedback Linearization ◽  
Large Scale ◽  
Stability Characteristic ◽  
Test Bed ◽  
Pendulum System ◽  
Large Scale Systems ◽  
Almost Disturbance Decoupling ◽  
Inverted Pendulum System ◽  
Disturbance Decoupling

The paper presents a novel feedback linearization controller of nonlinear multiinput multioutput time-delay large-scale systems to obtain both the tracking and almost disturbance decoupling (ADD) performances. The significant contribution of this paper is to build up a control law such that the overall closed-loop system is stable for given initial condition and bounded tracking trajectory with the input-to-state-stability characteristic and almost disturbance decoupling performance. We have simulated the two-inverted-pendulum system coupled by a spring for networked control systems which has been used as a test bed for the study of decentralized control of large-scale systems.

scholarly journals Extensive deep neural networks for transferring small scale learning to large scale systems

2019 ◽  
Vol 10 (15) ◽  
pp. 4129-4140 ◽  
Author(s):  
Kyle Mills ◽  
Kevin Ryczko ◽  
Iryna Luchak ◽  
Adam Domurad ◽  
Chris Beeler ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Large Scale ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Small Scale ◽  
Large Scale Systems ◽  
Energy Entropy ◽  
Large Systems ◽  
Number Of Particles

We present a physically-motivated topology of a deep neural network that can efficiently infer extensive parameters (such as energy, entropy, or number of particles) of arbitrarily large systems, doing so with scaling.

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