Intelligent Control for Access to Space

University of Strathclyde

PhD 3 years Glasgow, UK

Uploaded 1 Aug 2018

Job Description

Artificial intelligence (AI) as a field of research and development emerged and developed in parallel with the development of the theory of automatic control, starting in the decades that followed the WWII, with the first major applications in computing and information science, and later in automatic control. As today, both AI and automatic control have reached a level of stability and maturity, which, coupled with the sharp increase in possibilities of computer technology (both hardware and software), can lead to a rethinking of theory and practice of Intelligent Control (IC).

IC is again, after a period of neglect linked to various failures and unsuccessful attempts, the answer for control systems that have to ensure their optimality, functional and operational reliability, efficiency, fault tolerance and survivability when: 1) there is a lack of a priori information about the control object and external environment of its functioning, 2) there is a big number of aleatory factors that cannot be taken into account deterministically, and 3) there could be degradation (from failures, accidents) or necessity of targeted reconfiguration.

Countries that have concrete ambitions for access to space and military supremacy are heavily investing for research in this area, to design next generation of launch and re-entry vehicles, as well as next generation of high performance fighters aircraft, and strategic missiles and bombers.

The aim of this project is to investigate and develop novel IC methods for launch and re-entry vehicles.

The objectives are:

- develop methods and tools for the treatment of sensor data of launch and re-entry missions, affected by noise and errors;

- develop methods and tools for the robust and reliable autonomous control of launch and re-entry vehicles, considering both epistemic and aleatory uncertainties.

Person Specification


Master degree in aerospace/mechanical, computer science or applied mathematics


Experience in the field of trajectory analysis, machine learning, artificial intelligence and optimisation

Starting date

October 2018

Student eligibility

UK and EU students.

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