Similarity relations of PID flight control parameters of scaled-model and full-size aircraft

2022 ◽  
pp. 1-1
Author(s):  
Ting Yue ◽  
Xianshuai Zuo ◽  
Lixin Wang ◽  
Jianzhong Geng ◽  
Hong Zhang
Keyword(s):  
Flight Control ◽  
Control Parameters ◽  
Scaled Model ◽  
Full Size ◽  
Similarity Relations

Dynamic Optimization Genetic Algorithms for Flight Control Laws Design

2013 ◽  
Vol 278-280 ◽  
pp. 1581-1584
Author(s):  
Xiao Xiong Liu ◽  
Yan Wu ◽  
Peng Hui Li ◽  
Heng Xu
Keyword(s):  
Genetic Algorithms ◽  
Dynamic Optimization ◽  
Flight Control ◽  
Control Parameters ◽  
Longitudinal Control ◽  
Control Laws ◽  
Longitudinal Function ◽  
Adaptive Capability ◽  
Dynamic Optimization Model ◽  
Set Up

The general flight control laws are designed by static designs and dynamic fits. To improve the adaptive capability, the method of control laws design was introduced by using dynamic optimization genetic algorithms. The control parameters were adjusted online in the flight envelope. The dynamic optimization model was built for aircraft longitudinal function. The fitness was set up by applying order track. And then the control parameters were regulated by dynamic optimization genetic algorithms. Finally an example of a longitudinal control augmented stability system of an aircraft is used with a simulation.

A Comparison of Shape/Object Displays, Quasi Shape Displays, and Conventional Univariate Indicators: Integration Benefits or the “Nearer to Thee” Effect?

1987 ◽  
Vol 31 (5) ◽  
pp. 543-547 ◽  
Author(s):  
Dennis B. Beringer ◽  
Steven E. Chrísman
Keyword(s):  
Flight Control ◽  
Continuous System ◽  
System Control ◽  
Control Parameters ◽  
Secondary System ◽  
Detection Rates ◽  
Information Display ◽  
Physical Proximity ◽  
Control Information ◽  
Rate Information

The performance of a complex task, such as piloting an aircraft, requires an operator to effectively process and integrate information from numerous sources. Recent efforts (Beringer, 1985; 1986) have examined the combining of integrated displays to provide both continuous-system-control information and secondary system status/rate information. The present effort reported herein is an attempt to determine the origin of benefits that accrue from shape/object displays; specifically whether they stem from cognitively based integration of information or merely physical proximity of the information. This study examined several formats of information display using conventional (univariate needle indicators) and nonconventional (polar histograms and polar polygons) formats for presenting multiple univariate indices. Some flight control parameters and out-of-tolerance detection rates were affected by the format of the peripheral display while these performance measures plus processing rate of a digit-cancelling side task were affected by flight difficulty (two axes versus three axes; effect in the expected direction). Performance with the polar histogram display was superior in all cases followed closely by performance with the polar polygons, the multiple nonpolar univariate indicators (needles) being least effective for the differentiation task. This suggests that the shape/object displays do allow integration of information beyond that found with simple physical proximity of univariate indicators. A continuation of this research is addressing the problem of system diagnostics using these formats.

Extracting principal parameters of complex networks

2015 ◽  
Vol 26 (09) ◽  
pp. 1550103
Author(s):  
Yifang Ma ◽  
Zhiming Zheng
Keyword(s):  
Dynamic Systems ◽  
Growth Process ◽  
Dimensional Space ◽  
High Dimensional ◽  
Scale Function ◽  
Control Parameters ◽  
Diffusion Matrix ◽  
Similarity Relations ◽  
Low Dimensional ◽  
Network Ensembles

The evolution of networks or dynamic systems is controlled by many parameters in high-dimensional space, and it is crucial to extract the reduced and dominant ones in low-dimensional space. Here we consider the network ensemble, introduce a matrix resolvent scale function and apply it to a spectral approach to get the similarity relations between each pair of networks. The concept of Diffusion Maps is used to get the principal parameters, and we point out that the reduced dimensional principal parameters are captured by the low order eigenvectors of the diffusion matrix of the network ensemble. We validate our results by using two classical network ensembles and one dynamical network sequence via a cooperative Achlioptas growth process where an abrupt transition of the structures has been captured by our method. Our method provides a potential access to the pursuit of invisible control parameters of complex systems.

Predicting thrust of aircraft using artificial neural networks

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fatma Yildirim Dalkiran ◽  
Mustafa Toraman
Keyword(s):  
Flight Control ◽  
Data Sets ◽  
Training Algorithms ◽  
Control Parameters ◽  
Multi Layer Perceptron ◽  
Ann Model ◽  
Training Process ◽  
Content Type ◽  
Minimum Error ◽  
Ann Models

Purpose The purpose of this study is to make artificial neural network (ANN)-based prediction about thrust using the flight control parameters of aircrafts. Design/methodology/approach In today’s transportation, airplanes have an important place because of their safety, quality and speed. One of the most important parameters affecting the secure flying of aircrafts is the thrust value of aircraft engines. Determining the optimum thrust value should be investigated. If thrust value is less than optimum level, the flight safety runs a risk. Otherwise, fuel consumption goes high and some unwanted vibrations occur that cause uncomfortable flight. In this study, multi-layer perceptron ANNs, which are one of the intelligent optimization methods and frequently used in the literature, are preferred to predict the optimum thrust value during take-off, cruise and landing. The actual flight data, which is taken from the black box of an Airbus A319 aircraft, is used to train ANN models using back propagation algorithms. Velocity, altitude and ambient temperature values of the aircraft are selected as inputs and the thrust value is selected as output. During the training process of ANN, eight different training algorithms with different structures are used to figure out optimum ANN model with minimum error. Findings Different ANN models were trained using eight different training algorithms. The ANN model with minimum error has multi-layer perceptron structure, which is trained using Levenberg–Marquardt (LM) algorithm. Research limitations/implications To obtain the ANN structure with minimum error training, process takes more than a day depending on the capacity of a computer for LM training algorithm. But after training process, the trained ANN model produces sufficient output in a few milliseconds. Practical implications Totally 15,670 input-output data sets are obtained from an Airbus A319 aircraft. 12,889 of them are used as training data and the rest of the data sets, selected randomly are used as test data. Test data sets are never used in training phase, and the obtained results show that the ANN model successfully predicts thrust value using unseen input data. Social implications The ANN could be used as an alternative method to predict other flight control parameters of aircrafts. Originality/value To the best of authors’ knowledge, this study is the first example in literature to predict the thrust value of the aircraft using ANN.

Aerial Robot Formation Control via Pigeon-Inspired Optimization

2020 ◽  
pp. 1143-1180
Author(s):  
Haibin Duan
Keyword(s):  
Flight Control ◽  
Formation Control ◽  
Time Discretization ◽  
Population Based ◽  
Global Optimum ◽  
Formation Flight ◽  
Control Parameters ◽  
Aerial Robot ◽  
Swarm Intelligence Optimization ◽  
Prediction Control

Formation flight for aerial robots is a rather complicated global optimum problem. Three formation flight control problems are introduced in this chapter, respectively, underlying controller parameter optimization, basic formation control and formation reconfiguration control. Two methods, Model Prediction Control (MPC) and Control Parameterization and Time Discretization (CPTD), are applied to solve the above problems. However, the selection of appropriate control parameters is still a barrier. Pigeon-Inspired Optimization (PIO) is a new swarm intelligence optimization algorithm, which is inspired by the behavior of homing pigeons. Owning to its better performance of global exploration than others, the thoughts of PIO are applied to the control field to optimize the control parameters in the three aerial robot formation problems, to minimize the value of the cost function. Furthermore, comparative experimental results with a popular population-based algorithm called Particle Swarm Optimization (PSO) are given to show the feasibility, validity and superiority of PIO.

Research on the Simulation Model for Lateral Control Channel of FCS Based on PC

2013 ◽  
Vol 341-342 ◽  
pp. 995-998
Author(s):  
Qing Li ◽  
Wei Yang ◽  
Jie Zhou ◽  
Xiao Nan Ye
Keyword(s):  
Control System ◽  
Simulation Model ◽  
Flight Control ◽  
Control Channel ◽  
Control Parameters ◽  
Flight Control System ◽  
Lateral Control ◽  
Control Structures ◽  
Control Rules ◽  
Classical Control

According to the analysis of control structures of the two typical control modes-tilt control and course control, the simplified control rules for the two special control systems are presented. Under the condition of enduring the real-time property and fidelity, the classical control theory is applied to study the control parameters selecting of the flight control system (FCS) based on PC modeling traits. The selecting process of control parameters of lateral control channel is analyzed and the simulation resources are simplified. The simulation model is achieved. The steps are summarized for the simulation modeling of lateral control channel of the flight control system, and the corresponding flow chart based on PC is also given.

scholarly journals Aeroelastic scaling of flying demonstrator: flutter matching

2021 ◽  
Vol 22 ◽  
pp. 42
Author(s):  
Joan Mas Colomer ◽  
Nathalie Bartoli ◽  
Thierry Lefebvre ◽  
Joaquim R.R.A. Martins ◽  
Joseph Morlier
Keyword(s):  
Mach Number ◽  
Traditional Approach ◽  
Flow Conditions ◽  
Scaled Model ◽  
Full Size ◽  
Flutter Speed ◽  
Squared Error ◽  
The Common ◽  
Flow Similarity ◽  
Scaled Models

The traditional approach for the design of aeroelastically scaled models assumes that either there exists flow similarity between the full-size aircraft and the model, or that flow non-similarities have a negligible effect. However, when trying to reproduce the behavior of an airliner that flies at transonic conditions using a scaled model that flies at nearly-incompressible flow conditions, this assumption is no longer valid and both flutter speed and static aerodynamic loading are subject to large discrepancies. To address this issue, we present an optimization-based approach for wing planform design that matches the scaled flutter speeds and modes of the reference aircraft when the Mach number cannot be matched. This is achieved by minimizing the squared error between the full-size and scaled aerodynamic models. This method is validated using the Common Research Model wing at the reference aircraft Mach number. The error in flutter speed is computed using the same wing at model conditions, which are in the nearly-incompressible regime. Starting from the baseline wing, its planform is optimized to match the reference response despite different conditions, achieving a reduction of the error in the predicted flutter speed from 7.79% to 2.13%.

Research on the Simulation Model for Longitudinal Control Channel of FCS Based on PC

2013 ◽  
Vol 756-759 ◽  
pp. 564-568
Author(s):  
Qing Li ◽  
Wei Yang ◽  
Zhao Xie Huang
Keyword(s):  
Control System ◽  
Simulation Model ◽  
Flight Control ◽  
Control Channel ◽  
Control Parameters ◽  
Flight Control System ◽  
Longitudinal Control ◽  
Control Structures ◽  
Control Rules ◽  
Classical Control

According to the analysis of control structures of the two typical control modes-pitch control and height control, the simplified control rules for the two special control systems are presented. Under the condition of enduring the real-time property and fidelity, the classical control theory is applied to study the control parameters selecting of the flight control system (FCS) based on PC modeling traits. The selecting process of control parameters of longitudinal control channel is analyzed and the simulation resources are simplified. The simulation model is achieved. The steps are summarized for the simulation modeling of longitudinal control channel of the flight control system, and the corresponding flow chart based on PC is also given.

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