scholarly journals Neural Network Parameter Adjustment for Control Rules in Flight Control System

2012 ◽  
Author(s):  
Jing Zhou ◽  
Jun Tao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Flight Control ◽  
Flight Control System ◽  
Network Parameter ◽  
Parameter Adjustment ◽  
Control Rules

scholarly journals Adaptive and Resilient Flight Control System for a Small Unmanned Aerial System

2013 ◽  
Vol 2013 ◽  
pp. 1-25 ◽  
Author(s):  
Gonzalo Garcia ◽  
Shahriar Keshmiri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Parameter Identification ◽  
Real Time ◽  
Flight Control ◽  
Unmanned Aerial System ◽  
Flight Control System ◽  
Network Parameter ◽  
Artificial Neural

The main purpose of this paper is to develop an onboard adaptive and robust flight control system that improves control, stability, and survivability of a small unmanned aerial system in off-nominal or out-of-envelope conditions. The aerodynamics of aircraft associated with hazardous and adverse onboard conditions is inherently nonlinear and unsteady. The presented flight control system improves functionalities required to adapt the flight control in the presence of aircraft model uncertainties. The fault tolerant inner loop is enhanced by an adaptive real-time artificial neural network parameter identification to monitor important changes in the aircraft’s dynamics due to nonlinear and unsteady aerodynamics. The real-time artificial neural network parameter identification is done using the sliding mode learning concept and a modified version of the self-adaptive Levenberg algorithm. Numerically estimated stability and control derivatives are obtained by delta-based methods. New nonlinear guidance logic, stable in Lyapunov sense, is developed to guide the aircraft. The designed flight control system has better performance compared to a commercial off-the-shelf autopilot system in guiding and controlling an unmanned air system during a trajectory following.

scholarly journals Design and Analysis for Early Warning of Rotor UAV Based on Data-Driven DBN

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1350 ◽  
Author(s):  
Chen ◽  
Wu ◽  
Wu ◽  
Xiong ◽  
Han ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fault Diagnosis ◽  
Early Warning ◽  
Flight Control ◽  
Data Driven ◽  
Diagnostic Model ◽  
Flight Control System ◽  
Model Based ◽  
On Line

The unmanned aerial vehicle (UAV), which is a typical multi-sensor closed-loop flight control system, has the properties of multivariable, time-varying, strong coupling, and nonlinearity. Therefore, it is very difficult to obtain an accurate mathematical diagnostic model based on the traditional model-based method; this paper proposes a UAV sensor diagnostic method based on data-driven methods, which greatly improves the reliability of the rotor UAV nonlinear flight control system and achieves early warning. In order to realize the rapid on-line fault detection of the rotor UAV flight system and solve the problems of over-fitting, limited generalization, and long training time in the traditional shallow neural network for sensor fault diagnosis, a comprehensive fault diagnosis method based on deep belief network (DBN) is proposed. Using the DBN to replace the shallow neural network, a large amount of off-line historical sample data obtained from the rotor UAV are trained to obtain the optimal DBN network parameters and complete the on-line intelligent diagnosis to achieve the goal of early warning as possible as quickly. In the end, the two common faults of the UAV sensor, namely the stuck fault and the constant deviation fault, are simulated and compared with the back propagation (BP) neural network model represented by the shallow neural network to verify the effectiveness of the proposed method in the paper.

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.

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