New stability criteria for a class of systems arising in neural network control and fuzzy systems

2000 ◽  
Vol 214 (1) ◽  
pp. 1-8
Author(s):  
Y Fang ◽  
T G Kincaid ◽  
S Li
Keyword(s):  
Neural Network ◽  
Stability Criterion ◽  
Fuzzy Systems ◽  
Neural Control ◽  
Network Control ◽  
Neural Network Control ◽  
Stability Criteria ◽  
Simple Derivation ◽  
The Stability

In this paper, the stability of a class of systems arising from neural control and fuzzy systems is studied. A new unifying stability criterion is presented using a very simple derivation. This result generalizes some previous results; some easily testable conditions are obtained.

Neural network control design for a rigid-link electrically driven robot

2003 ◽  
Vol 217 (2) ◽  
pp. 99-107 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Neural Network ◽  
Control Design ◽  
Network Control ◽  
Second Step ◽  
Neural Network Control ◽  
Linear Dynamics ◽  
Rigid Link ◽  
Non Linear ◽  
Robot Systems ◽  
The Stability

In this paper, a back-stepping scheme for rigid-link electrically driven (RLED) robot systems is proposed. A two-step controller is presented: the first step is a virtual controller, while the second step is an actual one. A neural network is used to approximate the unknown non-linear dynamics in the system. The stability can be guaranteed by using a rigid proof. A simulation is used to illustrate the effectiveness of the proposed algorithm.

scholarly journals Adaptive Chebyshev Neural Network Control for Ventilator Model under the Complex Mine Environment

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ranhui Liu ◽  
Xinyan Hu ◽  
Chengyuan Zhang ◽  
Chuanxi Liu
Keyword(s):  
Neural Network ◽  
Closed Loop ◽  
Network Control ◽  
Neural Network Control ◽  
Complex Environment ◽  
Chebyshev Neural Network ◽  
Unknown Disturbance ◽  
The Stability ◽  
And Control ◽  
Motor Model

Ventilator is important equipment for mines as it safeguards the lives under the shaft and ensures other equipment’s proper functioning by providing fresh air. Therefore, how to effectively control the ventilator system becomes more significant. In order to acquire the commonly used model and control strategy for ventilator systems, a new universal ventilator model is established based on the blast capacity differential pressure in the ventilating duct and the ventilator motor model. Then, an adaptive Chebyshev neural network (ACNN) controller is proposed to effectively control the ventilator system where the unknown load torque and the unknown disturbance caused by the complex environment under the shaft are approximated by the Chebyshev neural network (CNN). Afterwards, an appropriate Lyapunov function candidate is designed to guarantee the stability of the proposed controller and the closed-loop ventilator system. Finally, the ACNN controller has been demonstrated to be effective in terms of validity and precision for the new proposed ventilator model through the simulations.

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