scholarly journals Learning quadrotor dynamics using neural network for flight control

2016 ◽  
Author(s):  
Somil Bansal ◽  
Anayo K. Akametalu ◽  
Frank J. Jiang ◽  
Forrest Laine ◽  
Claire J. Tomlin
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Quadrotor Dynamics

Research of the 3-DOF Helicopter System Based on Adaptive Inverse Control

2013 ◽  
Vol 389 ◽  
pp. 623-631 ◽  
Author(s):  
Xiu Yan Wang ◽  
Ying Wang ◽  
Zong Shuai Li
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Fuzzy Inference ◽  
Reference Model ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Actual System ◽  
Adaptive Inverse Control ◽  
Inverse Control ◽  
Network Connection

For the flight control problem occurred in 3-DOF Helicopter System, reference adaptive inverse control scheme based on Fuzzy Neural Network model is designed. Firstly, fuzzy inference process of identifier and controller is achieved by using the network structure. Meanwhile, the neural network connection weights are used to express parameters of fuzzy inference. Then, back-propagation algorithm is adopted to amend the network connection weights in order to automatically identify the fuzzy model and adjust its membership functions and parameters, so that the actual system output of adaptive inverse controller control which is adjusted can track the reference model output. Finally, the simulation result of 3-DOF Helicopter System based on the scheme shows that the method is effective and feasible.

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.

Design of Tiltrotor Flight Control System in Conversion Mode Using Improved Neutral Network PID

2013 ◽  
Vol 850-851 ◽  
pp. 640-643 ◽  
Author(s):  
Cheng Peng ◽  
Xin Min Wang ◽  
Xiao Chen
Keyword(s):  
Neural Network ◽  
Flight Control ◽  
Back Propagation ◽  
Neutral Network ◽  
Flight Control System ◽  
Nonlinear Characteristics ◽  
Conversion Mode ◽  
High Order Time ◽  
Neural Network Pid ◽  
Tiltrotor Aircraft

Considering the high-order, time-varying and nonlinear characteristics of the tiltrotor aircraft in conversion mode, the control requirements and the specific conversion scheme are presented first. An improved back propagation (BP) neural network PID control algorithm is then proposed, utilizing the strong nonlinear mapping characteristics of neural network. In the proposed algorithm, an inertia term is added to the gradient-descent-based weight adjustment formula of the conventional BP neutral network in order to increase the convergence rate and obtain satisfactory control effects. Finally simulation results indicate the effectiveness of the proposed algorihm.

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