scholarly journals Integrated Design of Autonomous Orbit Determination and Orbit Control for GEO Satellite Based on Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Youtao Gao ◽  
Zhicheng You ◽  
Bo Xu
Keyword(s):  
Neural Network ◽  
Orbit Determination ◽  
Design Method ◽  
Integrated Design ◽  
Satellite System ◽  
Orbit Control ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Geo Satellite ◽  
Autonomous Orbit Determination

In order to improve the autonomy of a maneuvered GEO satellite which is a member of a navigation satellite system, an integrated design method of autonomous orbit determination and autonomous control was proposed. A neural network state observer was designed to estimate the state of the GEO satellite, with only the intersatellite ranging information as observations. The controller is determined autonomously by another neural network based on the estimated state and the preset correction trajectory. A gradient descent learning method with a forgetting factor was used to derive the weight updating strategy which can satisfy the system’s stability and real-time performance. A Lyapunov method was used to prove the stability of both the observer and the controller. The neural network observer can reduce the influence of control on autonomous orbit determination. The neural network controller can improve the robustness of the maneuvered GEO satellite. The simulation results show the effectiveness of this method.

scholarly journals Autonomous Orbit Determination for Lagrangian Navigation Satellite Based on Neural Network Based State Observer

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Youtao Gao ◽  
Tanran Zhao ◽  
Bingyu Jin ◽  
Junkang Chen ◽  
Bo Xu
Keyword(s):  
Neural Network ◽  
Orbit Determination ◽  
Transition Matrix ◽  
State Observer ◽  
Nonlinear Perturbations ◽  
The Neural Network ◽  
Autonomous Orbit Determination ◽  
Simulation Results ◽  
Libration Orbits

In order to improve the accuracy of the dynamical model used in the orbit determination of the Lagrangian navigation satellites, the nonlinear perturbations acting on Lagrangian navigation satellites are estimated by a neural network. A neural network based state observer is applied to autonomously determine the orbits of Lagrangian navigation satellites using only satellite-to-satellite range. This autonomous orbit determination method does not require linearizing the dynamical mode. There is no need to calculate the transition matrix. It is proved that three satellite-to-satellite ranges are needed using this method; therefore, the navigation constellation should include four Lagrangian navigation satellites at least. Four satellites orbiting on the collinear libration orbits are chosen to construct a constellation which is used to demonstrate the utility of this method. Simulation results illustrate that the stable error of autonomous orbit determination is about 10 m. The perturbation can be estimated by the neural network.

The Intelligent Controller Design of Parallel Online Mixing and Supplying Glue System

2010 ◽  
Vol 44-47 ◽  
pp. 4089-4093 ◽  
Author(s):  
Fan Hu Meng ◽  
Li Ping Sun ◽  
Liang Kuan Zhu
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Controller Design ◽  
Design Method ◽  
Favorable Effect ◽  
Intelligent Controller ◽  
Advantages And Disadvantages ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

In this paper, a novel control method by using parallel online mixing and supplying technology for glue system with multi-input, strong coupling and nonlinear was provided. The neural network controller design method is adopted to design the intelligent controller, in which one as the main controller and the other one as the identifier to revise main controller. Both advantages and disadvantages of the two neural network controllers were analyzed. Simulation results showed that both methods obtained a more favorable effect than the traditional control method.

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

Neural Networks L2-Gain Controller Design for Nonlinear System

2011 ◽  
Vol 467-469 ◽  
pp. 1505-1510
Author(s):  
Dan Liu ◽  
Ni Hong Wang ◽  
Gui Ying Li
Keyword(s):  
Neural Network ◽  
Disturbance Rejection ◽  
Differential Inequality ◽  
Controller Design ◽  
Design Method ◽  
General Structure ◽  
The Neural Network ◽  
Network Weight ◽  
Finite Gain ◽  
L2 Gain

This paper proposes a new method that it uses the neural network to construct the solution of the Hamiltion-Jacobi inequality (HJ), and it carries on the optimization of the neural network weight using the genetic algorithm. This method causes the Lyapunov function to satisfy the HJ, avoides solving the HJ parital differential inequality, and overcomes the difficulty which the HJ parital differential inequality analysis. Beside this, it proposes a design method of a nonlinear state feedback L2-gain disturbance rejection controller based on HJ, and introduces general structure of L2-gain disturbance rejection controller in the form of neural network. The simulation demonstrates the design of controller is feasible and the closed-loop system ensures a finite gain between the disturbance and the output.

Guidepost-based autonomous orbit determination method for GEO satellite

2020 ◽  
Author(s):  
Bowen Hou ◽  
Jiongqi Wang ◽  
Haiyin Zhou ◽  
Zhangming He ◽  
Dong Li ◽  
...  
Keyword(s):  
Orbit Determination ◽  
Determination Method ◽  
Geo Satellite ◽  
Autonomous Orbit Determination

Design and Realization of Experimental Autonomous Driving System Based on Neural Network Control

2012 ◽  
Vol 241-244 ◽  
pp. 1953-1958
Author(s):  
Qing Fu Kong ◽  
Fan Ming Zeng ◽  
Jie Chang Wu ◽  
Jia Ming Wu
Keyword(s):  
Neural Network ◽  
Autonomous Driving ◽  
Network Control ◽  
Neural Network Control ◽  
Test Results ◽  
Driving System ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Autonomous Driving System

Intelligent vehicle is an attractive solution to the traffic problems caused by automobiles. An experimental autonomous driving system based on a slot car set is designed and realized in the paper. With the application of a wireless camera equipped on the slot car, the track information is acquired and sent to the controlling computer. A backpropogation (BP) neural network controller is built to imitate the way of player’s thinking. After being trained, the neural network controller can give the proper voltage instructions to the direct current (DC) motor of the slot car according to different track conditions. Test results prove that the development of the autonomous driving system is successful.

scholarly journals Weighted Multiple-Model Neural Network Adaptive Control for Robotic Manipulators with Jumping Parameters

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Jiazhi Li ◽  
Weicun Zhang ◽  
Quanmin Zhu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
External Disturbance ◽  
Lyapunov Stability Theory ◽  
Robotic Manipulators ◽  
Adaptive Observer ◽  
Multiple Model ◽  
Neural Network Controller ◽  
The Neural Network ◽  
The Stability

This study addresses the tracking control issue for n-link robotic manipulators with largely jumping parameters. Based on radial basis function neural networks (RBFNNs), we propose weighted multiple-model neural network adaptive control (WMNNAC) approach. To cover the variation ranges of the parameters, different models of robotic are constructed. Then, the corresponding local neural network controller is constructed, in which the neural network has been used to approximate the uncertainty part of the control law, and an adaptive observer is implemented to estimate the true external disturbance. The WMNNAC strategy with improved weighting algorithm is adopted to ensure the tracking performance of the robotic manipulator system when parameters jump largely. Through the Lyapunov stability theory and the method of virtual equivalent system (VES), the stability of the closed-loop system is proved. Finally, the simulation results of a two-link manipulator verify the feasibility and efficiency of the proposed WMNNAC strategy.

Neural Network Controlled High-Step Up Based Single-Phase PV Inverter System with Improved Dynamic Response

2017 ◽  
Vol 14 (1) ◽  
pp. 421-429
Author(s):  
M. L Bharathi ◽  
D Kirubakaran
Keyword(s):  
Neural Network ◽  
Dynamic Response ◽  
Single Phase ◽  
Closed Loop ◽  
Simulation Studies ◽  
Loop Control ◽  
Pv Inverter ◽  
Simulink Model ◽  
Neural Network Controller ◽  
The Neural Network

In AC utility closed loop high step up based single phase PV inverter system is preferred because of fast dynamic response. This paper deals with comparison of dynamic response of the PI and the neural network controlled single phase PV inverter systems. The DC output from the solar cell is stepped up using a high step up converter and the output is converted into AC using an inverter. A change in the insolation is considered in the present investigation. Simulation studies are conducted using MATLAB. The closed loop control systems using the PI controller and the neural network controller are investigated and their results are compared. The Simulink model for the above system are developed and they are successfully used for simulation studies. The hardware is fabricated, tested and the practical results matches with the simulation results.

A Neural Network Method for Miniature Unmanned Helicopter Heading Control

2012 ◽  
Vol 468-471 ◽  
pp. 93-96
Author(s):  
Meng Bai ◽  
Min Hua Li
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Network Control ◽  
Neural Network Control ◽  
Unmanned Helicopter ◽  
Unknown Parameters ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Heading Control ◽  
Helicopter Aerodynamics

A neural network control method for heading control of miniature unmanned helicopter is proposed. For the complexity of miniature helicopter aerodynamics, it is difficult to identify the unknown parameters of yaw dynamics model. To design heading controller of miniature helicopter without modelling yaw dynamics, BP neural network is designed as heading controller, which is trained by collected flight data. By training, the neural network controller can learn the artificial operation strategy and realize the heading control of miniature unmanned helicopter. Simulation results demonstrate the validity of the proposed neural network control method.

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