DeepAI AI Chat
Log In Sign Up

Neural Network Approach to Railway Stand Lateral Skew Control

02/28/2014
by   Peter Mark Benes, et al.
0

The paper presents a study of an adaptive approach to lateral skew control for an experimental railway stand. The preliminary experiments with the real experimental railway stand and simulations with its 3-D mechanical model, indicates difficulties of model-based control of the device. Thus, use of neural networks for identification and control of lateral skew shall be investigated. This paper focuses on real-data based modeling of the railway stand by various neural network models, i.e; linear neural unit and quadratic neural unit architectures. Furthermore, training methods of these neural architectures as such, real-time-recurrent-learning and a variation of back-propagation-through-time are examined, accompanied by a discussion of the produced experimental results.

READ FULL TEXT
08/17/2012

Modeling and Control of CSTR using Model based Neural Network Predictive Control

This paper presents a predictive control strategy based on neural networ...
04/22/2022

A Data-Efficient Model-Based Learning Framework for the Closed-Loop Control of Continuum Robots

Traditional dynamic models of continuum robots are in general computatio...
03/11/2018

Modeling Singing F0 With Neural Network Driven Transition-Sustain Models

This study focuses on generating fundamental frequency (F0) curves of si...
10/21/2020

An Efficient Real-Time NMPC for Quadrotor Position Control under Communication Time-Delay

The advances in computer processor technology have enabled the applicati...