Self-learning Neural Network Control System for Physical Model with One Degree of Freedom of System of Active Vibration Isolation and Pointing of Payload Spacecraft

2017 ◽  
pp. 213-230 ◽  
Author(s):  
S. N. Sayapin ◽  
Yu. N. Artemenko ◽  
S. V. Panteleev
Keyword(s):  
Neural Network ◽  
Control System ◽  
Physical Model ◽  
Vibration Isolation ◽  
Network Control ◽  
Degree Of Freedom ◽  
Neural Network Control ◽  
Network Control System ◽  
Active Vibration ◽  
Self Learning

Backstepping sliding mode neural network control system for hypersonic vehicle

2019 ◽  
Vol 27 (11) ◽  
pp. 2392-2401
Author(s):  
刘 蓉 LIU Rong ◽  
黄大庆 HUANG Da-qing ◽  
姜定国 JIANG Ding-guo
Keyword(s):  
Neural Network ◽  
Control System ◽  
Sliding Mode ◽  
Hypersonic Vehicle ◽  
Network Control ◽  
Neural Network Control ◽  
Network Control System

Study on Multivariable System Based on PID Neural Network Control

2012 ◽  
Vol 591-593 ◽  
pp. 1490-1495 ◽  
Author(s):  
Huai Lin Shu ◽  
Jin Tian Hu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Transfer Function ◽  
Design Process ◽  
Network Control ◽  
Neural Network Control ◽  
Single Variable ◽  
Multivariable System ◽  
Pid Neural Network ◽  
Self Learning

The multivariable PID neural network (MPIDNN) control system is introduced in this paper. MPIDNN is used to perform both the control and the decouple at the same time and to get better performance. It is difficult to control multivariable system by conventional controller because the strong coupling properties of the system. Generally, the decoupling system should be designed first and the multivariable object would be divided into several single variable objects. Then, several simple controller would achieve the control of those objects. The decoupling system and the controller exist in theory but the design process is very difficult actually because the transfer function of the object is difficult to get. Especially, if the number of the object inputs is not equal to that of the object outputs, which is called unsymmetry object, the conventional decoupling is impossible. A actual example is discussed in the paper in order to prove the function of the MPIDNN, in which an un-symmetry multivariable system which has 3 inputs and 2 outputs is controlled by a MPIDNN and the perfect control property is obtained by self-learning process.

Adaptive Fuzzy Neural Network Control System in Cylindrical Grinding Process

2010 ◽  
Vol 426-427 ◽  
pp. 220-224
Author(s):  
X.M. Li ◽  
Ning Ding
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Network Control ◽  
Grinding Process ◽  
Neural Network Control ◽  
Network Control System ◽  
Cylindrical Grinding ◽  
Adaptive Fuzzy Neural Network ◽  
Fuzzy Neural

An adaptive fuzzy neural network control system in cylindrical grinding process was proposed. In this system, the initial cylindrical grinding parameters were decided by the expert system based on fuzzy neural network. Multi-feed and setting overshoot optimization methods were also adopted during the grinding process, and a human machine cooperation system (composed of human and two fuzzy – neural networks) could revise the process parameters in real-time. The experiment of the cylindrical grinding was implemented. The results showed that this control system was valid, and could greatly improve the cylindrical grinding quality and machining efficiency.

scholarly journals Ship roll damping via direct inverse neural network control system

2018 ◽  
Vol 57 (4) ◽  
pp. 2951-2960 ◽  
Author(s):  
Taher Awad ◽  
Mohamed Abd-elfatah Elgohary ◽  
Tawfik Elemam Mohamed
Keyword(s):  
Neural Network ◽  
Control System ◽  
Network Control ◽  
Neural Network Control ◽  
Network Control System ◽  
Roll Damping ◽  
Ship Roll
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