DEVELOPMENT OF NEURO-CONTROLLER BASED ON STM 32

2020 ◽  
Vol 13 ◽  
Author(s):  
O. Sinkevych ◽  
L. Monastyrskii ◽  
Ya. Boyko ◽  
B. Sokolovskii
Keyword(s):  
Neuro Controller

scholarly journals Adaptive Neuro-Controller Based on Hybrid Multi Layered Perceptron Network for Dynamic Systems

2012 ◽  
Vol 2 (3) ◽  
pp. 34-41 ◽  
Author(s):  
S. M. Sharun ◽  
M. Y.Mashor ◽  
W. N.H. Wan Jaafar ◽  
N. Mohd Nazid ◽  
S. Yaacob
Keyword(s):  
Dynamic Systems ◽  
Neuro Controller

scholarly journals A NEURAL FUZZY INFERENCE BASED ADAPTIVE CONTROLLER FOR NONHOLONOMIC ROBOTS

2011 ◽  
pp. 56-65
Author(s):  
Ting Wang ◽  
Fabien Gautero ◽  
Christophe Sabourin ◽  
Kurosh Madani
Keyword(s):  
Control Strategy ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Adaptive Controller ◽  
Reference Trajectory ◽  
Inference System ◽  
Nonholonomic Robots ◽  
Real Robot ◽  
Neuro Controller ◽  
Neural Fuzzy

In this paper, we propose a control strategy for a nonholonomic robot which is based on an Adaptive Neural Fuzzy Inference System. The neuro-controller makes it possible the robot track a desired reference trajectory. After a short reminder about Adaptive Neural Fuzzy Inference System, we describe the control strategy which is used on our virtual nonholonomic robot. And finally, we give the simulations’ results where the robot have to pass into a narrow path as well as the first validation results concerning the implementation of the proposed concepts on real robot.

scholarly journals Improving the Performance of Process Controllers Using a New Clustered Neural Network

2011 ◽  
Vol 1 ◽  
pp. 273-277
Author(s):  
M. Reza Soleymani Yazdi ◽  
Michel Guillot
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Operating Conditions ◽  
Machining Processes ◽  
Workpiece Material ◽  
Control Scheme ◽  
Organization Capacity ◽  
First Results ◽  
On Line ◽  
Neuro Controller

This paper presents first a newly developed clustered neural network, which incorporates self-organization capacity into the well-known common multilayer perceptron (MLP) architecture. With this addition, it is possible to reduce significantly overall memory degradation of the neuro-controller during on-line training. In the second part of the paper, this clustered multilayer perceptron (CMLP) network is applied and compared to the MLP through modeling and simulations of machining processes. Simulation results presented using machining data demonstrate that the CMLP possesses more powerful modeling capacity than the standard MLP, offers better adaptability to new operating conditions, and finally performs more reliably. During on-line training with machining data about 65% degradation of previously learned information can be observed in the MLP as opposed to only 11% for the CMLP. Finally, an adaptive control scheme intended for on-line optimization of the machining processes is presented. This scheme uses a feed forward CMLP inverse neuro-controller which learns off-line and on-line the relationships between process inputs and output under simulated perturbations (i.e., tool wear and non-homogeneous workpiece material properties). The first results using the CMLP inverse neuro-controller are promising

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