© 2012 Anwendungszentrum GmbH Oberpfaffenhofen Idea by: Dr. Eng. Mohamed Zayan | 1

© 2012 Anwendungszentrum GmbH Oberpfaffenhofen Idea by: Dr. Eng. Mohamed Zayan | 3 Spacecraft Control Using Adaptive Neural-Network Predictive Controllers (ANNPC) and GNSS Signals  This idea makes use of Adaptive Neural-Network Predictive Controllers (ANNPC) in conjunction with GNSS signals to control the orbit and attitude of any type of Earth orbiting spacecraft.  The simulation models we have developed demonstrate that one can implement an orbital control system for spacecraft by combining ANNPC with input state vectors generated from GNSS signals received on board.  The key advantage of using ANNPC is that it does not require highly accurate and costly dynamic models for specific spacecraft to enable orbital and attitude prediction and control for every new spacecraft design

© 2012 Anwendungszentrum GmbH Oberpfaffenhofen Idea by: Dr. Eng. Mohamed Zayan | 4 Spacecraft Control Using Adaptive Neural-Network Predictive Controllers (ANNPC) and GNSS Signals  Instead, a generic ANNPC algorithm can be developed and trained to learn the orbital and AOCS dynamics of spacecraft during their preoperational and operational phases.  The simulations have demonstrated that using such a system optimizes spacecraft thrust forces, thus reducing fuel consumption and prolonging missions by more than 30%.  By using ANNPC-GNSS, it is possible to reduce, or even eliminate, the reliance on ground control station (GCS) telemetry and ranging and tracking antenna (TTAC) systems (TTAC accounts for up to 50% of GCS costs).

© 2012 Anwendungszentrum GmbH Oberpfaffenhofen Idea by: Dr. Eng. Mohamed Zayan | 5 LEO with ANNPC-GNSS GEO with ANNPC-GNSS EARTH Technical description Autonomous Spacecraft Control- using GNSS signals Diagram Acknowledgment A. Garcia-Rodriguez GNSS-SSV` ICG WG-B, Vienna 06/06/2012

© 2012 Anwendungszentrum GmbH Oberpfaffenhofen Idea by: Dr. Eng. Mohamed Zayan | 6 EARTH Technical description Autonomous Spacecraft Control- using GNSS signals

© 2012 Anwendungszentrum GmbH Oberpfaffenhofen Idea by: Dr. Eng. Mohamed Zayan | 7 Technical description Autonomous Spacecraft Control- using GNSS signals Spacecraft Control Using Adaptive Neural Networks Predictive Controllers (ANNPC) and GNSS  The ANNPC simulation model which, has been developed, demonstrated that one can implement an efficient onboard AOCS and orbital control system for a spacecraft in LEO, and GEO by combining ANNPC with input state vectors generated from the onboard received GNSS signals.  The key feature of using ANNPC is that it is not necessary to developed highly accurate, thus complex, specific dynamic spacecraft models to enable orbital and attitude prediction and control for every new type of spacecraft design  Instead, a generic ANNPC algorithm can be developed which can be trained to learn the orbital and AOCS dynamics of the satellite during the pre-operational and operational phase of the spacecraft ranging.  This will enable the design and production of cost effective autonomous spacecrafts with highly efficient operational resource management.  The AANPC combined with GNSS accuracy will have the capability to cancel the systematic and random errors in the measurements thanks to the adaptive characteristic of the Neural Network, and predictive control capability this preventing any abrupt behaviour due the measurement spurious errors present in traditional methods.

© 2012 Anwendungszentrum GmbH Oberpfaffenhofen Idea by: Dr. Eng. Mohamed Zayan | 8 Technical description

© 2012 Anwendungszentrum GmbH Oberpfaffenhofen Idea by: Dr. Eng. Mohamed Zayan | 9 Business case  The target market is aerospace industry. However, the nature of the proposed ANNPC-GNSS system means that it can easily be ported to other applications requiring automated and autonomous navigation.  The generic ANNPC-GNSS solution will enable the cost effective design and production of different types of autonomous spacecrafts Orbital and AOCS systems without the need to resort to costly and time consuming generation of advanced flight dynamic modeling required by traditional methods.  The ambiguity resolution in position measurement using traditional TTAC ranging is around 1000 meters, compared to the ambiguity when using GNSSr, which is expected to be around 1 meter.  The maneuver efficiency shall be improved by at least 30 % thanks to high accuracy GNSS measurements, and ANNPC robustness. Which would translate to an equivalent saving in fuel.  Cost savings in the ground station side can be achieved by removing the need for a TTAC antenna and associated ranging system which contribute to at least 50% of the cost of the ground station.

© 2012 Anwendungszentrum GmbH Oberpfaffenhofen Idea by: Dr. Eng. Mohamed Zayan | 10 Current status  The Idea is simulated and tested in mathematical simulation model in model based design engineering model using MATLAB Package and Simulink.  Orbits Design for Remote Sensing Satellite, Zayan, M.A.; Eltohamy, F. Aerospace Conference, 2008 IEEE, Orbits Design for Remote Sensing SatelliteAerospace Conference, 2008 IEEE  Satellite orbits guidance using state space neural network, Zayan, M.A. Aerospace Conference, 2006 IEEE, Satellite orbits guidance using state space neural networkAerospace Conference, 2006 IEEE  Resources minimization in the satellite navigation process Zayan. M.A., Aerospace Conference, 2006 IEEE Resources minimization in the satellite navigation processAerospace Conference, 2006 IEEE  Satellite orbits control using adaptive neural networks predictive controllers (ANNPC), Aly, A.F.; Aly, M.N.; Zayan, M.A. Aerospace Conference, Proceedings IEEE, Satellite orbits control using adaptive neural networks predictive controllers (ANNPC)Aerospace Conference, Proceedings IEEE  Optimization techniques for orbit estimation and determination to control the satellite motion, Aly, A.F.; Nguib Aly, M.; Elshishtawy, M.E.; Zayan, M.A,.Aerospace Conference Proceedings, IEEE Optimization techniques for orbit estimation and determination to control the satellite motion  Book Title “Satellite Orbits Estimation, Determination, and Control”, Mohamed Zayan Lambert Academic Publisher Germany, ISBN: ,2010  The innovator (Dr. Eng. Mohamed Zayan) submitted his idea as an individual researcher with more the 15 years of experience in Aerospace industry and research. He developed the original ANNPC concepts as part of his PhD work.  In his current innovation he has adapted his algorithms to incorporate GNSS data. He holds the position of Satellites Control Station Manager at Nilesat -Egypt.

© 2012 Anwendungszentrum GmbH Oberpfaffenhofen Idea by: Dr. Eng. Mohamed Zayan | 11 Requirements / Next steps  It is very difficult to fully validate a spacecraft orbital and AOCS system on the ground. The obvious solution is to produce a low cost spacecraft, such as a cube sat or mini-sat, which incorporate a prototype system that can then be validated in space. Alternatively, incorporate the ANNPC-GNSS system as a parallel implementation to a traditional system on a planned satellite, where the prototype system can be tested, while the traditional system can be used to safeguard against any unforeseen or problematic situations. This would require:  Transferring the simulation models into a embedded system target software/firmware/hardware, an optimal combination will be selected.  Develop engineering model using the above system in conjunction with a orbital control simulation environment and realistic GNSS signals.  Develop a cube-sat based flight model based on the above system. An estimated fund of 600,000 € (not including launch costs) and 1.5y is expected for developing, launching and in orbit validation.  The possibility of developing a generic ANNPC-GNSS solution which can be applied to any unmanned vehicle, other than spacecrafts would require additional funding.

ArabMENA: Ulrike Daniels, Thorsten Rudolph, Minister Dr Wolfgang Heubisch and Dr Mohamed Zayan

© 2012 Anwendungszentrum GmbH Oberpfaffenhofen25/26 September 2012 | 13

© 2012 Anwendungszentrum GmbH Oberpfaffenhofen Idea by: Dr. Eng. Mohamed Zayan | 14 Thank You Questions are welcomed Dr. Mohamed Ahmed Zayan