Neural Network NARMAX Model Based Unmanned Aircraft Control Surface Reconfiguration

2016 ◽  
Author(s):  
Shangqiu Shan ◽  
Zhongxi Hou
Keyword(s):  
Neural Network ◽  
Unmanned Aircraft ◽  
Control Surface ◽  
Aircraft Control ◽  
Model Based

scholarly journals Reconfiguration of Unmanned Aircraft Control System

2017 ◽  
Vol 2017 (2) ◽  
pp. 80-96
Author(s):  
Marcin Żugaj
Keyword(s):  
Control System ◽  
Unmanned Aircraft ◽  
Control Surface ◽  
Control Input ◽  
Aircraft Control ◽  
System Failures ◽  
System Reconfiguration ◽  
And Control ◽  
Control Surfaces ◽  
Aircraft Control System

Abstract Reliability of unmanned aircraft is a decisive factor for conducting air tasks in controlled airspace. One of the means used to improve unmanned aircraft reliability is reconfiguration of the control system, which will allow to maintain control over the aircraft despite occurring failures. The control system is reconfigured by using operational control surfaces, to compensate for failure consequences and to control the damaged aircraft. Development of effective reconfiguration algorithms involves utilization of a non-linear model of unmanned aircraft dynamics, in which deflection of each control surface can be controlled independently. The paper presents a method for an unmanned aircraft control system reconfiguration utilizing a linear and nonlinear model of aerodynamic loads due to control. It presents reconfiguration algorithms, which differ with used models and with optimization criteria for deflections of failure-free control surfaces. Additionally it presents results of a benchmark of the developed algorithms, for various types of control system failures and control input.

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

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