Position Control Based on Static Neural Networks of Anthropomorphic Robotic Fingers

2006 ◽  
pp. 888-897 ◽  
Author(s):  
Juan Ignacio Mulero-Martínez ◽  
Francisco García-Córdova ◽  
Juan López-Coronado
Keyword(s):  
Neural Networks ◽  
Position Control ◽  
Static Neural Networks ◽  
Robotic Fingers

scholarly journals Stability and Bifurcation Analysis for a Class of Generalized Reaction-Diffusion Neural Networks with Time Delay

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Tianshi Lv ◽  
Qintao Gan ◽  
Qikai Zhu
Keyword(s):  
Neural Networks ◽  
Local Field ◽  
Neumann Boundary ◽  
Reaction Diffusion ◽  
Normal Form Theory ◽  
Center Manifold Reduction ◽  
Static Neural Networks ◽  
Stability And Bifurcation ◽  
Uniform Steady State ◽  
The Stability

Considering the fact that results for static neural networks are much more scare than results for local field neural networks and our purpose letting the problem researched be more general in many aspects, in this paper, a generalized neural networks model which includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks is built and the stability and bifurcation problems for it are investigated under Neumann boundary conditions. First, by discussing the corresponding characteristic equations, the local stability of the trivial uniform steady state is discussed and the existence of Hopf bifurcations is shown. By using the normal form theory and the center manifold reduction of partial function differential equations, explicit formulae which determine the direction and stability of bifurcating periodic solutions are acquired. Finally, numerical simulations show the results.

New Results on Stability for a Class of Fractional-Order Static Neural Networks

2020 ◽  
Vol 39 (12) ◽  
pp. 5926-5950
Author(s):  
Xiangqian Yao ◽  
Meilan Tang ◽  
Fengxian Wang ◽  
Zhijian Ye ◽  
Xinge Liu
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Static Neural Networks

Adaptive Neural Network in Compensation the Dynamics and Position Control of a Servo-Hydraulic System With a Flexible Load

2005 ◽  
Author(s):  
Hassan Yousefi ◽  
Heikki Handroos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Hydraulic System ◽  
Position Control ◽  
Reference Model ◽  
Differential Evolution Algorithm ◽  
Nonlinear Behavior ◽  
System Stability ◽  
Hydraulic Systems ◽  
Highly Nonlinear

Asymmetrical servo-hydraulic systems are commonly used in industry. These kinds of systems are nonlinear in nature and generally difficult to control. Because of changing system parameters, using the same gain will cause overshoot or even loss of system stability. The highly nonlinear behavior of these devises makes them idea subjects for applying different types of sophisticated controllers. This paper is concerned with using two artificial neural networks in compensation the dynamics and position tracking of a second order model reference in a flexible servo-hydraulic system. In present study, a neural network as an acceleration feedforward and another one as a gain scheduling of a proportional controller are proposed. Differential evolution algorithm is used to find the weights and biases to avoid the local minima. The proposed controller was verified with a commonly used p-controller. The results suggest that if the neural networks choose and train well, they improve all performance evaluation criteria such as stability, fast response, and accurate reference model tracking in servo-hydraulic systems.

Condition Monitoring of an Electrohydraulic Position Control System Using Artificial Neural Networks

2004 ◽  
Author(s):  
K. Pollmeier ◽  
C. R. Burrows ◽  
K. A. Edge
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Condition Monitoring ◽  
Position Control ◽  
Linear Actuator ◽  
Modular Approach ◽  
Failure Characteristics ◽  
Actuator System ◽  
Artificial Neural ◽  
Position Control System

This paper investigates the condition monitoring of a servo-valve-controlled linear actuator system using artificial neural networks (NNs). The aim is to discuss techniques for the identification of failure characteristics and their source. It is shown that neural networks can be trained to identify more than one fault but these are larger and require more training patterns than networks for single fault diagnosis. This leads to much longer training times and to problems with scaleability. Therefore a modular approach has been developed. Several networks were trained each to identify an individual fault. The parallel outputs of these nets were then used as inputs to another network. This additional network was able to identify not only the correct faults but also the actual fault levels.

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