NDLI: Spacecraft power system controller based on neural network

Content Provider	IEEE Xplore Digital Library
Author	Fahmy, F.H. El-madany, H.T. El-Rahman, N.M.A. Dorrah, H.T.
Copyright Year	2010
Description	Author affiliation: Electrical Power & Machines Dept., Faculty of Engineering, Cairo University, Cairo, Egypt (Dorrah, H.T.) \|\| Potovoltaic Cells Dept., Electronics Research Institute, National Research, Center Building, Cairo, Egypt (Fahmy, F.H.; El-madany, H.T.; El-Rahman, N.M.A.)
Abstract	Neural control is a branch of the general field of intelligent control, which is based on the concept of artificial intelligence. This work presents the spacecraft orbit determination, dimensioning of the renewable power system, and mathematical modeling of spacecraft power system which are required for simulating the system. The complete system is simulated using MATLAB–SIMULINK. The NN controller out perform PID in the extreme range of non-linearity. Well trained neural controller can operate at different conditions of load current at different orbital periods without any tuning such in case of PID controller. So an artificial neural network (ANN) based model has been developed for the optimum operation of spacecraft power system. An ANN is trained using a back propagation with Levenberg–Marquardt algorithm. The best validation performance is obtained for mean square error is equal to 9.9962 × $10^{–11}$ at epoch 637. The regression between the network output and the corresponding target is equal to 100% which means a high accuracy. NNC architecture gives satisfactory results with small number of neurons, hence better in terms of memory and time are required for NNC implementation. The results indicate that the proposed control unit using ANN can be successfully used for controlling the spacecraft power system in low earth orbit (LEO). Therefore, this technique is going to be a very useful tool for the interested designers in space field.
Starting Page	227
Ending Page	231
File Size	137099
Page Count	5
File Format	PDF
ISBN	9781424477654
e-ISBN	9781424477661
DOI	10.1109/ICCCENG.2010.5560447
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-01
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Space vehicles Satellites Control Artificial neural networks Ni-Cd battery Spacecraft Photovoltaic array Batteries Power systems Neural network Arrays Load modeling
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Artificial neural network based load forecasting

Adapted multilayer feedforward ANN based power management control of solar photovoltaic and wind integrated power system

A neural network model for Ni-Cd batteries

A hybrid fuzzy logic and neural network controller for spacecraft docking system

A Solar-powered Battery Charger with Neural Network Maximum Power Point Tracking Implemented on a Low-Cost PIC-microcontroller

Modeling and Control of a Network of Cooperative Satellites Using Neural Networks

Modeling Ni-MH battery based on immune evolutionary network

Modeling and simulation of grid-connected hybrid photovoltaic/battery distributed generation system

Complete electrical model and simulation of a medium size satellite

Spacecraft power system controller based on neural network

Similar Documents

Artificial neural network based load forecasting

Adapted multilayer feedforward ANN based power management control of solar photovoltaic and wind integrated power system

A neural network model for Ni-Cd batteries

A hybrid fuzzy logic and neural network controller for spacecraft docking system

A Solar-powered Battery Charger with Neural Network Maximum Power Point Tracking Implemented on a Low-Cost PIC-microcontroller

Modeling and Control of a Network of Cooperative Satellites Using Neural Networks

Modeling Ni-MH battery based on immune evolutionary network

Modeling and simulation of grid-connected hybrid photovoltaic/battery distributed generation system

Complete electrical model and simulation of a medium size satellite

Spacecraft power system controller based on neural network