Hybrid Neural Network Controller Using Adaptation Algorithm

2007 ◽  
pp. 148-157
Author(s):  
ManJun Cai ◽  
JinCun Liu ◽  
GuangJun Tian ◽  
XueJian Zhang ◽  
TiHua Wu
Keyword(s):  
Neural Network ◽  
Adaptation Algorithm ◽  
Hybrid Neural Network ◽  
Neural Network Controller ◽  
Network Controller

A stable neural adaptive force controller for a hydraulic actuator

2003 ◽  
Vol 217 (4) ◽  
pp. 303-310 ◽  
Author(s):  
B Daachi ◽  
A Benallegue
Keyword(s):  
Neural Network ◽  
Hydraulic System ◽  
A Priori ◽  
Experimental Plant ◽  
Hydraulic Actuator ◽  
Adaptation Algorithm ◽  
Lyapunov Approach ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

In this paper a neural network adaptive force controller is proposed for a hydraulic system. The dynamic model of this system is highly non-linear and very complex to obtain. Thus, it is considered as a black box and a priori identification becomes necessary. A neural network is used to approximate the model and then a controller using the Lyapunov approach is designed. The neural network parameters are updated online according to an adaptation algorithm obtained via stability analysis. The performance of the proposed neural network controller is validated on an experimental plant.

Stable Neural Network Controller Based Observer for Rigid Robot Manipulators

2003 ◽  
Vol 15 (1) ◽  
pp. 77-83 ◽  
Author(s):  
Boubaker Daâchi ◽  
◽  
Abdelaziz Benallegue
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Robot Manipulators ◽  
Stability Study ◽  
Control Law ◽  
Neural Net ◽  
Adaptation Algorithm ◽  
Neural Network Controller ◽  
Network Controller ◽  
Joint Velocity

We propose a neural network controller using only joint position measurements for rigid robot manipulators. The joint velocity needed for the control law is estimated using an observer based on sliding mode. A decomposed structure neural network approximates the unknown model of the system. Each neural network (MLP) approximates a separate element of the dynamical model. These approximations are used to conduct an adaptive stable control law. The TaylorYoung series was used to solve the nonlinearity problem of the MLP and to lead to the parameters adaptation algorithm. The corresponding parameters are the weights of the neural net. They are updated via the adaptation algorithm derived from stability study of the system in closed loop using the Lyapunov approach and intrinsic properties of robot manipulators. Simulations were conducted to show the conductance of the proposed controller.

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

Sign in / Sign up

Export Citation Format

Share Document