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Real-time optimal control of quadrocopters using deep representations of the optimal state feedback

Fri, 07/08/2022 - 16:15
Start Date: 
2018
Programme: 
Discovery
End Date: 
2019
Programme Reference: 
18-8510
Country: 
The Netherlands
Contractor: 
TU DELFT
Status: 
Closed
Description: 

A major challenge in the field of control is to achieve reliable, aggressive, high-speed control of autonomous vehicles. In space, this may involve spacecraft that need to land under harsh conditions, or even – in an extreme scenario – negotiating asteroid debris fields at high speeds. On Earth, the exemplar task that draws most attention currently is high-speed autonomous flight of drones. The application of optimal control on board limited platforms has been severely hindered by the large computational requirements of current state-of-the-art implementations.

In this work, we introduced and applied a deep neural network to directly map the robot states to the optimal control actions to overcome this limitation. The approach has been illustrated with high-speed flight of a drone with heavily constrained onboard processing, and can be applied to other platforms such as spacecraft that have similar restrictions.

Application Domain: 
Exploration
Technology Domain: 
13 - Automation, Telepresence & Robotics
10 - Flight Dynamics and GNSS
9 - Mission Operation and Ground Data Systems
5 - Space System Control
2 - Space System Software
Competence Domain: 
3-Avionic Systems
6-Life & Physical Science Payloads, Life Support, Robotics & Automation
7-Propulsion, Space Transportation and Re-entry Vehicles
8-Ground Systems and Mission Operations
Keywords: 
optimal control
quadrocopters
Autonomy
Executive summary: