Research of Nonlinear Adaptive Flight Control Using Inversible System of Neural Networks

2014 ◽  
Vol 490-491 ◽  
pp. 960-963
Author(s):  
Shao Song Wan ◽  
Jian Cao ◽  
Cen Rui Ma ◽  
Cong Yan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stability Analysis ◽  
Flight Control ◽  
Feedback Linearization ◽  
Adaptive Neural Networks ◽  
Adaptive Flight Control ◽  
The Status ◽  
Training Structure

This paper discusses training structure and procedure about inversible system of neural network. Feedback linearization and adaptive neural networks provide a powerful controller architecture. Finally, this paper surveys the status of nonlinear, and adaptive flight control, and summarizes the research being conducted in this area. A description of the controller architecture and associated stability analysis is given.

scholarly journals Tracking of periodic oscillations in an underactuated system via adaptive neural networks

2018 ◽  
Vol 37 (1) ◽  
pp. 128-143 ◽  
Author(s):  
Sergio A Puga-Guzmán ◽  
Carlos Aguilar-Avelar ◽  
Javier Moreno-Valenzuela ◽  
Víctor Santibáñez
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mechanical System ◽  
Feedback Linearization ◽  
Control Technique ◽  
Underactuated System ◽  
Periodic Oscillations ◽  
Underactuated Mechanical System ◽  
Model Based ◽  
Adaptive Neural Networks

In this paper, the tracking control of periodic oscillations in an underactuated mechanical system is discussed. The proposed scheme is derived from the feedback linearization control technique and adaptive neural networks are used to estimate the unknown dynamics and to compensate uncertainties. The proposed neural network-based controller is applied to the Furuta pendulum, which is a nonlinear and nonminimum phase underactuated mechanical system with two degrees of freedom. The new neural network-based controller is experimentally compared with respect to its model-based version. Results indicated that the proposed neural algorithm performs better than the model-based controller, showing that the real-time adaptation of the neural network weights successfully estimates the unknown dynamics and compensates uncertainties in the experimental platform.

scholarly journals Implementación de un software para el análisis de imágenes aéreas multiespectrales de caña de azúcar [Implementation of software for the analysis of multispectral aerial images sugarcane]

2013 ◽  
Author(s):  
Gerardo Schneider ◽  
Alejandro Javier Hadad ◽  
Alejandra Kemerer
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Crop Productivity ◽  
Software Implementation ◽  
Aerial Images ◽  
The Status

Resumen En este trabajo se presenta una implementación de software para la determinación del estado de plantaciones de caña de azúcar basado en el análisis de imágenes aéreas multiespectrales. En la actualidad no existen técnicas precisas para estimar objetivamente la superficie de caña caída o volcada, y esta ocasiona importantes pérdidas de productividad en la cosecha y en la industrialización. Para la realización de éste trabajo se confeccionó un dataset referencial de imágenes, y se implementó un software a partir del cual se obtuvieron indicadores propuestos como representativos del fenómeno agronómico, y se realizaron análisis de los datos generados. Además se implementó un software clasificador referencial basado en redes neuronales con el que se estimó la fortaleza de dichos indicadores y se estimó la superficie afectada en forma cuantitativa y espacial. Palabras ClavesCaña de azúcar, cuantificación, volcado, red neuronal, procesamiento de imagen   Abstract In this paper we present a software implementation for determining the status of sugarcane plantations based on the analysis of multispectral aerial images. Currently there are no precise techniques to estimate objectively the cane area fall or overturned, and this causes significant losses in crop productivity and industrialization. For the realization of this work a dataset benchmark images was made, and a software, from which were obtained representative proposed indicators for the agronomic phenomenon was implemented, and analyzes of the data generated were realized. In addition, we implemented a software benchmark classifier based on neural networks with which we estimated the strength of these indicators and the area affected was estimated quantitatively and spatially. Keywords Sugarcane, quantification, fall, neural network, image processing

Adaptive Feedback Linearization Control of SISO Nonlinear Processes Using a Self-Generating Neural Network-Based Approach

2011 ◽  
Vol 6 (1) ◽  
Author(s):  
Karim Salahshoor ◽  
Amin Sabet Kamalabady
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Adaptive Control ◽  
Feedback Linearization ◽  
Learning Algorithm ◽  
Rbf Neural Networks ◽  
Adaptive Feedback ◽  
Control Scheme ◽  
Feedback Linearization Control ◽  
Adaptive Control Scheme

This paper presents a new adaptive control scheme based on feedback linearization technique for single-input, single-output (SISO) processes with nonlinear time-varying dynamic characteristics. The proposed scheme utilizes a modified growing and pruning radial basis function (MGAP-RBF) neural network (NN) to adaptively identify two self-generating RBF neural networks for online realization of a well-known affine model structure. An extended Kalman filter (EKF) learning algorithm is developed for parameter adaptation of the MGAP-RBF neural networks. The MGAP-RBF growing and pruning criteria have been endeavored to enhance its performance for online dynamic model identification purposes. A stability analysis has been provided to ensure the asymptotic convergence of the proposed adaptive control scheme using Lyapunov criterion. Capabilities of the adaptive feedback linearization control scheme is evaluated on two nonlinear CSTR benchmark processes, demonstrating good performances for both set-point tracking and disturbance rejection objectives.

scholarly journals APPLICATION OF ARTIFICIAL NEURAL NETWORKS IN CONTROL ALGORITHMS OF ROBOTIC SYSTEMS

2020 ◽  
Vol 3 (156) ◽  
pp. 46-48
Author(s):  
D. Zubenko
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Stability Analysis ◽  
Discrete Time ◽  
Continuous Time ◽  
Control Design ◽  
Stability And Convergence ◽  
Switching Systems ◽  
Language Recognition ◽  
Artificial Neural

The problem of stability analysis for the general class of random pulsed and switching neural networks is presented in this paper, which is to be investigated both continuous dynamics and impulsive jumps of random disturbances. Two numerical examples are used to explain and highlight the effectiveness of the results developed.The purpose of this article is to provide a comprehensive overview of studies, including continuous time and discrete time models for solving various problems, and their application in motion planning and superfluous manipulator management, chaotic system tracking, or even population control in mathematical biological sciences. Considering the fact that real-time performance is in demand for time-varying problems in practice, analysis of the stability and convergence of various models with continuous time is considered in a unified form in detail. In the case of solving the problems of discrete time, procedures are summarized for how to discriminate a continuous model and methods for obtaining an accuracy decision. Due to its strong ability to extract features and autonomous learning, neural networks are rooted in many industries, for example. neuroscience, mathematics, informatics and engineering, transport, etc. Despite their widespread use in various fields, such as artificial intelligence, language recognition, and computer simulation, the issue of neural network stability analysis is the most primary and fundamental that has attracted intense attention in recent decades.and references therein. It is well known that pulse and switching systems are formulated by combining pulse systems with switching systems, which is a more complex model of nonlinear systems. With their increasing use in network management, power systems, and the like, impulse control theory and switching systems have been a hot topic of research for the past decade. The fruitful results of research on stability analysis and control design of pulse and switching systemssuch as input stability, time-limited, controllability and observation and feedback control design, etc. On the other hand, it is also noteworthy. Keywords: artificial neural network, electric transport, numerical algorithms, control reliability

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