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Control Technologies at ESOC: Current Projects and Future Perspectives

Control Technologies at ESOC: Current Projects and Future Perspectives. European Space Operations Centre of ESA Darmstadt, Germany. Contact Point: Alessandro.Donati@esa.int (TOS-OSC Control Technologies Unit @ ESA/ESOC). PLANET Technologietag – 16.6.2003, Ulm. Content. Definition of Terms

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Control Technologies at ESOC: Current Projects and Future Perspectives

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  1. Control Technologies at ESOC: Current Projects and Future Perspectives European Space Operations Centre of ESA Darmstadt, Germany Contact Point: Alessandro.Donati@esa.int (TOS-OSC Control Technologies Unit @ ESA/ESOC) PLANET Technologietag – 16.6.2003, Ulm

  2. Content • Definition of Terms • Effectively Introducing Innovation • The Motivation • The Environment • The Methodology • TOS-OSC Modus Operandi • Problem Cases: Recent Developments • ENVISAT Gyro Monitoring Tool • Optimal INTEGRAL Reaction Wheels Bias Manoeuvre • XMM/INTEGRAL Radiation Monitoring & Operational Adjustment • PROBA Autonomous (Re-)Scheduler • Prototype Scheduler for Ground Station Operations • Expected Benefits and Lessons Learnt • Medium & Long Term Vision

  3. Definitions of Terms • Mission Control Processes include: • Planning & Scheduling, • Monitoring, Diagnostic & Control • Resource Management & Off-line Analysis • Simulation & Training • Mission Control R&D Process is • the process of efficiently and effectively introducing innovation in specific Mission Control Processes where this is justified and needed. • Mission Control Teams include: • Flight Control Team (spacecraft) • Ground Control Team (ground segment) • Flight Dynamics Team

  4. Effectively Introducing Innovation: Motivation • Enhancement of the overall Mission Control system performance : • For meeting increasing demands (new functions) from ongoing and future missions • For reducing cost and/or risk • Contribution to the modernisation of ESOC’s mission control approach • Provide European and National Mission Control Centres Innovative and Validated Technical Solutions to Specific Problems

  5. Effectively Introducing Innovation: Automation of Control Processes • Mission Control Processes encompass: • Humans • Machines (Hardware & Software) • Procedures • Innovation in Mission Control Processes can support automation of routine activities: Humans Machines Procedures

  6. Effectively Introducing Innovation: the Environment • Mission Control Teams members are the users and final beneficiaries of the improvements provided by the Mission Control R&D Process • Mission Control Teams are very sensitive to potential risks introduced by “system changes”, for obvious reasons related to the criticality of spacecraft operations; • Mission Control Teams members devote marginal manpower resources to support innovation due to current limitation of available manpower; • Availability of financial resources is very limited

  7. Effectively Introducing Innovation: the Methodology Approach derived from the Dynamic System Development Method(*) and adapted to our “environment”: • Iterative incremental prototyping • Competitive selection of User-defined cases • User is part of the development team • Frequent time-fixed deliveries with features implemented according to priorities negotiated for each time-box(**) • Iterative risk assessment • Maximum re-use of available resources (open source S/W, infrastructure) • Scalable solutions (**) see next chart (*) http://www.dsdm.org

  8. Effectively Introducing Innovation: the Methodology DSDM Time Boxing Development slots fixed in allocated time and resources; variable in implemented functionalities; Time box content & priorities is negotiated at each iteration. • Must haves are essential, the minimum usable subset, without them the objectives are missed • Should haves are really needed, but when you miss them you can define a workaround • Could haves still included, but you can easily do without them • Won't haves Not enough value to include in this increment of development (these are usually greater than 40%) (*) Time boxing is a technique based on the fact that over 40% of custom built software is NEVER used. This together with the fact that most projects traditionally deliver too late to address the needs they were designed to address.

  9. Effectively Introducing Innovation: TOS-OSC modus operandi Future MissionsStudy Teams Project Case R&D Spin-in Universities Industry Technology Flight/Ground Control Teams Project Teams Conferences Seminars Prototype Implementation In-houseLectures,Training Operational Validation ProofedSolution • Library of reusable solutions, algorithms and techniques Infrastructure

  10. Case #1: ENVISAT Gyro Performance Monitoring Tool • PROBLEM: enhance the capability to monitor ENVISAT gyros behaviour, detect the anomaly signatures at an earlier stage, when no standard alarming (OOL or FDIR) is yet triggered & automate the reporting process. • EXPECTED BENEFITS: automate the gyro monitoring tasks, early detection of potential degradation patterns, smoothing the possible gyro replacing process w/o affecting spacecraft payload productivity and reducing operators stress level vs. sudden unexpected degradations • IMPLEMENTED SOLUTION: operational prototype making use of past ERS-1/2 operational experience, coded with fuzzy logic diagnostic inference engine; off-line history database for all ENVISAT gyros data • STATUS: currently under extended operational validation; in March 03 a correct and punctual detection of a slight noise increase affecting gyroscope #1 measurements validated the capability of the tool.

  11. Case #1: ENVISAT Gyro Performance Monitoring Tool

  12. Case #2: Optimal XMM/INTEGRAL Reaction Wheels Bias Manoeuvre • PROBLEM: enhance the current optimisation approach for identifying the initial reaction wheels speed at the beginning of each orbit (perigee), able to support all scheduled observations, at minimum resource usage. • EXPECTED BENEFITS: save onboard fuel, extend mission lifetime & increase mission return • IMPLEMENTED SOLUTION: operational prototype with data import & export for FD formats, for XMM & INTEGRAL missions. Optimisation algorithms using either classic genetic algorithm or multi-objective genetic algorithm • STATUS: initial operational validation completed with equivalent fuel saving of around 35%, using multi-objective GA (XMM and INTEGRAL cases); Flight Dynamics plan to use the tool in middle 2003 for INTEGRAL.

  13. Case #2: Optimal XMM/INTEGRAL Reaction Wheels Bias Manoeuvre

  14. Case #3: Radiation Monitoring & Operational Adjustments • PROBLEM: enhance the capability to monitor INTEGRAL/XMM radiation environment and support operational decision making process for payload & sensors reconfigurations. • EXPECTED BENEFITS: reduce uncertainty gap of instrument operability in heavy radiation conditions; forecast short term radiation level evolution (e.g. Van Allen crossing, solar flares waves); enhance spacecraft safety and productivity levels: • reduced instrument exposure risk, increased observation return • IMPLEMENTED SOLUTION: initial prototype making use of on-board radiation history and real-time data, complemented by external measurements (NOAA); forecast engine based on dynamic numerical modelling techniques and artificial neural network • STATUS: version for XMM delivered and under acceptance phase; on-going fine tuning of the implemented algorithm & human-machine interface.

  15. Case #4: PROBA Autonomous (Re-) Scheduler • PROBLEM: Allow the on-board data handling subsystem to constantly monitor the successful execution of scheduled tasks and in case of resources unavailability or new activity requests autonomously reschedule the tasks on queue, respecting the stated constraints. • EXPECTED BENEFITS: introduce a higher level of on-board autonomy, increase the spacecraft productivity • IMPLEMENTED SOLUTION: ground based dynamic scheduler prototype with conflict detection, multi criteria decision making capability and dynamic context-sensitive ranking (conflict resolution); • potential for upgrading to an on-board software implementation and to validate it within the extended Proba operational lifetime. • STATUS: final prototype delivered to Redu. Operational validation campaign due to start.

  16. Case #4: PROBA Autonomous (Re-) Scheduler • Implementation of an onboard smart scheduler which can: • Allocate activities to satisfy a set of given goals. • Identify and solve conflicts between resources and housekeeping or requested activities with no person in the loop. • Reschedule activities, whenever necessary, by working in almost real-time. • Requisites: the final allocation must always: • Be consistent with current temporal and resource constraints. • Converge in a finite time.

  17. Case #4: PROBA Autonomous (Re-) Scheduler • User’s Goal: users can specify high level goals and the system should be able to achieve them considering all the resource constraints. • Example: • Goal: take a picture of the region X of the Earth in a certain window time [t1, t2]. • Output: • Reschedule the pending activities so the new request can be scheduled. • If the memory is full plan first a downlink to dump it. • Perform attitude manoeuvre, lower vibration in the spacecraft, warm up the payload, make sure the required energy is available, etc…

  18. Case #4: PROBA Autonomous (Re-) Scheduler • Architecture: • The knowledge base is pre-processed off-line. • New goals (user-defined or self-defined) are inserted in the current scheduling definition. • A first allocation of the activities is done using constructive methods (CSP). Usually the new scenario is over constrained, so a cost function is used to guide the search. • Then, the conflicts are solved using Multi-Attribute Decision Making (MADM) and Approximate Reasoning (fuzzy logic). • After that, the state transitions are checked to obtain a consistent scheduling scenario. • The new scheduling scenario is executed.

  19. Case #4: PROBA Autonomous (Re-) Scheduler • Current Status: • Final prototype delivered to Redu. Operational validation campaign due to start. • Potential Future Work: • Design and implementation of an onboard autonomous scheduler

  20. Case #5: Prototype Scheduler for Ground Station Operations • The Kiruna Ground Station tracks several spacecrafts using two antennas and ground station connection equipments. • Every mission team issues a request to the Kiruna ground station to book a certain number of temporal slots. • The mission team is aware of the next passes. • Every mission team is totally unaware of the requests issued by the other mission teams and issues its request as if it was the only user of the ground station services. • User’s Goal: check that all requests are satisfied and produce a schedule completely free of resource conflicts. If this is not possible, it allocates first the activities with higher priority.

  21. Case #5: Prototype Scheduler for Ground Station Operations • The resource conflict detection module checks the existence of conflicts in the proposed schedule. • The detected conflicts are solved using two different approaches: • Back-Tracking Approach: • Allocate activities until there is a conflict. In this case it goes one step back and tries with a different activity. If none of the remaining activities produces a feasible schedule it goes another step back, and so on. • Slow algorithm: tries every possibility with the use of heuristics to improve the performance.

  22. Case #5: Prototype Scheduler for Ground Station Operations • Genetic Algorithms Approach: • Artificial intelligence technique based on natural evolution. • Codification (potential solution): ordered list of the activities to perform in the next slots (schedule). • Optimization: minimize the sum of priorities of the activities not allocated. • Mutation: swaps activities or groups of activities. • Fast algorithm in finding the best solution, however it is not guaranteed that every possibility is tried.

  23. Case #5: Prototype Scheduler for Ground Station Operations • Current Status: • Initial prototype finalized and tested with operational data. • Potential Future Work: • Design and implementation of a conflict detection and resolution module for an integrated ground station planning and scheduling tool

  24. Expected Benefits & Lessons Learnt • Artificial Intelligent techniques CAN provide benefits in improving Mission Control Processes in • efficiency • capabilities • A major area is decision making process in existence of unsharp input parameters or activity conflicts • User-driven fast iterative prototyping & ”operational prototype” final delivery are instrumental to bridge the gap between Academic world and “Operational” world • Facilitate focusing on the highest priority functions • Enable operational use of risk mitigation • Rationalise use of limited resources • Availability of historical data is often a pre-requisite

  25. Medium & Long-term Vision • Positive experience and encouraging results generate expectation of increase of Project Cases in number and complexity • In 5-year time we expect to have a consolidated class of solved problem cases to become an infrastructure asset ready for re-use • Expected increase of level of automation and performance of ground systems, at acceptable risk • Expected integration of currently split functional systems • Migration of proven and validated intelligent solution from ground to space: augmented on-board autonomy capability

  26. Conclusions • The European Space Operations Centre of ESA is pursuing continuous improvement of its mission control processes in terms of cost efficiency and augmented functionalities: • Artificial intelligence and advanced control technologies play a significant role in specific problem cases • Positive measurable results provide comfort in further exploitation of artificial intelligence to serve mission control processes Thank you for your interest ! Feedback: alessandro.donati@esa.int

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