ESA Open Invitation to Tender AO10403
Open Date: 10/07/2020
Closing Date: 18/09/2020 13:00:00

Status: ISSUED
Reference Nr.: 20.1EC.03
Prog. Ref.: Technology Developme
Budget Ref.: E/0901-01 – Technology Developme
Special Prov.: BE+DK+FR+DE+IT+NL+ES+SE+CH+GB+IE+AT+NO+FI+PT+GR+LU+CZ+RO+PL+EE+HU
Tender Type: C
Price Range: 200-500 KEURO
Products: Satellites & Probes / AOCS & GNC / AOCS & GNC Eng. SW / SW for AOCS&GNC design, analysis, simulation, etc.
Technology Domains: Space System Control / Control Techniques and Tools / Advanced Control, Estimation & Optimisation
Establishment: ESTEC
Directorate: Directorate of Tech, Eng. & Quality
Department: Systems Department
Contract Officer: Seynaeve, Christophe Rene R.
Industrial Policy Measure: N/A – Not apply
Last Update Date: 10/07/2020
Update Reason: Tender issue

To augment GNC systems by using Artificial Intelligence techniques in order to improve the performances, flexibility, autonomy and capability to handle failures and performance degradation of GNC systems while satisfying the reliability standards of a safety critical space system. To use AI techniques to design and analyse GNC systems. The performance of currently available Guidance Navigation and Control (GNC) systems for space is limited by unmodelled effects or uncertain parameters (e.g. sensor/actuator performances and performance degradations). Artificial Intelligence (AI) techniques based on machine learning could be integrated in GNC systems in order to enable on-line learning of the properties of dynamics, environment, sensors and actuators that limit the available performances. Such AI-assisted GNC system would increase both the performance and the degree of autonomy available on-board in terms of robustness, adaptability and awareness. However, typical AI techniques do not meet the reliability standards forsafety critical spacesystems (e.g. reproducibility, robustness, computational efficiency, guaranteed convergence properties). It is needed to investigate the criticalities and challenges of the use of AI techniques for GNC design, implementation and verification. In particular, the integration of AI techniques into a typical GNC control framework (covering classical and modern control techniques) shall be analysed. The focus of the activity is on integrating techniques based on model knowledge (physical system and architectural configuration)with data-based on-line learning. Robustness and compatibility with or need for adaptation of the available control design and analysis frameworks and established qualification processes is to be assessed. The resulting AI-supported GNC system will be more flexible, adaptable and more performant than a classical GNC system, while still meeting the standards of a safety critical space system. This activity entails the following tasks:- review formal mathematical approaches to develop a robust and explainable AI technology- establish the functional and performance requirements applicable to an AI-assisted GNC design process and to an AI-augmented GNC system- perform trade-off of suitable mathematical AI approaches compatible with the current GNC architectures and design processes(model-based approach)- establish the AI techniques suitable to the modelling, control and verification needs in the view of a robust and explainable AI-supported GNC architectures and functions- develop a prototype set of benchmark problems for AI-assisted GNCdesign and AI-augment GNC system and for AI-supported autonomy- perform a detailed design and coding of the established AI techniques applied to AI-assisted GNC design and to AI-augment GNC system- assess the performance and robustness of the AI-assisted GNC system- define way forward for AI-GNC system deploymentSoftware shall be delivered under an ESA Software Community Licence