- Country: Netherlands
- Summary: Robust Real-Time Constrained Optimal Control Using Machine Learning
- NLT Ref No: 104486446
- Deadline: 28 Aug 2024
- Financier: Other Funding Agencies
- Purchaser Ownership: Government
- Tender Value: Refer Document
- Notice Type: Tender
- Document Ref. No.: 1-12406
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- Description:
- 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)
- Documents:
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CLICK HERE TO LOGINRobust Real-Time Constrained Optimal Control Using Machine Learning - Netherlands Tender
The EUROPEAN SPACE RESEARCH AND TECHNOLOGY CENTRE, a Government sector organization in Netherlands, has announced a new tender for Robust Real-Time Constrained Optimal Control Using Machine Learning. This tender is published on NetherlandsTenders under NLT Ref No: 104486446 and is categorized as a Tender. Interested and eligible suppliers are invited to participate by reviewing the tender documents and submitting their bids before the deadline on 2024-08-28.
The estimated tender value is Refer Document, and full details, including technical specifications and submission requirements, are provided in the official tender documents. Ensure all submissions meet the criteria outlined to be considered for evaluation.
