March 6, 2024
Europe/Amsterdam timezone

List of technologies

This is the list of technologies that will be covered during the workshop.


Real-time optimised guidance

To ensure real-time adaptability in the face of failures, changing conditions, no-fly zones and abort scenarios, future launchers must be able to autonomously compute or update its trajectory. The execution of complex orbital manoeuvres also benefits from this capability. Trajectory generation involves solving a nonlinear optimisation problem, but the past few years have seen the development of algorithms that achieve this in a reliable and efficient manner.


Guidance and control co-design

Current GNC design processes rely on the high-level separation between guidance and control functions. With the next generation of launchers, much tighter dynamical interactions between (online-generated) trajectory and attitude are arising, rendering the aforementioned separation impractical if not unfeasible. This leads to the need for a full GNC architecture and design review aimed at maximising the achievable performance and autonomy level.


Enhanced sensor fusion

Future launchers shall optimally exploit and fuse sensor information to ensure highly accurate state estimates and resilience in case of failure. These two properties are enablers for critical functions such as online stage fall-down propagation and precise retropropulsive landing. State-of-practice fusion schemes combine GNSS and INS signals, yet this can be extended to other sensors, such as optical cameras, LiDAR, star trackers and load sensors.


Control using onboard wind information

Wind is a major contributor to the aerodynamic loads that the launcher’s structure must be able to withstand. Nonetheless, the real-time incorporation of wind information in the GNC design is not yet considered in current systems. Future launchers shall feature dynamical filters or onboard sensors that provide reliable estimates of the wind disturbances, which will then enable improved load management, increased responsiveness and safer operations.


Onboard system identification

Future launchers should be aware of their dynamic characteristics in real-time, including aerodynamic coefficients, mass properties, flexible and slosh modes, actuator dynamics, etc. This is a critical input for the effective adaptation of GNC algorithms to the actual characteristics of the vehicle. Mismatches between expected and online-identified vehicle models arise from the high level of modelling uncertainty, but also from subsystem failures and changing conditions.


Onboard health & performance monitoring

Future launchers must be resilient to (sub)system degradation, hence they must incorporate mechanisms to actively monitor the health of flight-critical modules. By relying on novel techniques, the next generation of launchers should reliably detect when a fault has occurred and identify its source and severity. This information can be used to take actions such as reconfiguring the GNC system to minimise performance degradation or switching to a safe mode.


Onboard safety margin prediction

To support decision-making capabilities, future launchers must be able to accurately predict how a certain action or set of alternatives will impact the mission safety margins (e.g. stability, flight envelope, structural loads, stage fall-down area). In the case of a failure, any recovery action (including flight termination) must ensure that these margins remain satisfied. This prediction can be carried out using methods such as real-time propagation of uncertain sets and robust performance analysis.


MVM with enhanced decision-making

MVM (Mission & Vehicle Management) is the unit that implements the launcher’s autonomy layer, triggering different modes and managing equipment configurations. MVM decision-making involves optimising complex large-scale problems, which limits the effectiveness of pure model-based methods. Enhanced decision-making capabilities can be achieved by supplementing these methods with the latest developments in the area of machine learning and artificial intelligence.


Fault-tolerant guidance and control

Once a fault is detected, the next generation of launchers shall be able to perform an optimisation of the available resources to ensure minimal degradation while stability/performance boundaries remain satisfied. At control level, recovery actions may include switching between predefined control structures and parameters or adapting to online-identified dynamics. At guidance level, the system may be able to re-compute a trajectory that copes with the impaired scenario.


Adaptable robust control

While pure adaptive controllers (e.g. MRAC) are known for their practical limitations in terms of certification, and robust controllers are traditionally over conservative in terms of stability, it is possible to use the robust control framework to design and validate control systems that vary (within a prescribed set) as a function of onboard vehicle conditions estimates. This is coined "adaptable" control and allows future launchers to increase both their achievable performance and mission versatility.


Robust safety control

While pure robust control design provides formal guarantees of stability and performance, it does not allow to explicitly prevent the system from leaving a prescribed safety set, such as flight envelope and keep-out zones. Safety sets can be enforced under uncertain and noisy conditions using e.g. control barrier functions. This approach has seen several successful applications in the field of robotics, but has not yet been applied to aerospace systems.