Robust Real-Time Constrained Optimal Control Using Machine Learning Tender, Netherlands - 104486446

Procurement Summary

Country : Netherlands

Summary : Robust Real-Time Constrained Optimal Control Using Machine Learning

Deadline : 28 Aug 2024

Other Information

Notice Type : Tender

TOT Ref.No.: 104486446

Document Ref. No. : 1-12406

Financier : Other Funding Agencies

Purchaser Ownership : Public

Tender Value : Refer Document

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Tender Details

Tenders are invited for Robust Real-Time Constrained Optimal Control Using Machine Learning.

Open Date: 16/07/2024 17:13 CET

Closing Date: 28/08/2024 13:00 CET

Price Range 500 KEURO

Prog. Reference: E/0904-611 - GSTP Element 1 Dev

Technology Keywords: 5-C-II-Advanced Control, Estimation & Optimisation

The Objective of this Activity is to Leverage Machine Learning Methods to Augment the Performance of Robust Motion Planning and Control Methods Applicable to a Wide Range of Space Applications, Such as Autonomous Planetary and Asteroid Landing, Spacecraft Rendezvous and Proximity Operations, among Others. Description: The Ability to Autonomously Plan and Execute Highly Constrained Manoeuvres will be a Critical Enabler for Many Future Space Missions Ranging from Planetary Landers, Deep Space Probes, Large Space Constellations and Robotic Service Vehicles. By Leveraging Autonomous Planning Capabilities, Spacecraft can Safely Cope with Dynamic Environments, Failures Scenarios, Complex objectives, and Stringent Constraints. Autonomous Planning and Control Typically Rely on a Model of the Spacecraft and its Environment to Make Predictions about the Future and Optimize Certain objectives Subject to Various Constraints on the System State and Control Inputs. To be Useful for Control or Guidance Purposes, These Computations Need to be Performed within a Short Time and on the Limited Computational Resources Available on Modern Spacecraft. Furthermore, to Guarantee Safety and Reliability at System Level, Proper Care should be Taken into Account for the Inevitable Physics Modelling and State Estimation Errors. When Prioritizing Factors Other than Execution Time, One can Leverage State-of-the-Art Optimization-Based Heuristics to Seek Viable and High-Accuracy solutions for These Intricate Robust Motion Planning Challenges. However, Performing These Computations on-Board the Spacecraft in a Reasonable Amount of Time Remains a Challenge for Non-Trivial Planning Problems. Furthermore, These Heuristics Typically Rely on an Initial Guess of the Solution that is Iteratively Refined Using Numerical Methods. Therefore, the Performance of These Motion Planning Algorithms can be Highly Sensitive to the Quality of the Initial Guess. If a Feasible Initial Guess, (i.e. Satisfying All the Hard Constraints of the Planning Problem)

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