About the project
You will design control policies for autonomous systems combining control, optimization, and machine learning tools. The challenges include limited computing resources to run for example complex neural networks models, or limited communication resources for example in networks of robots, and the safety and robustness analysis of the learned policies.
There is an ongoing transformation in engineering autonomous systems that aim to achieve complex objectives with limited human intervention in applications such as robotics, self-driving cars, and industrial systems. While the design of autonomous systems has typically relied on pre-defined models, the desire to operate in complex, unknown, or varying conditions implies that models of the system and the operating environment may not be always available. As a result, machine learning and data-driven approaches are on the rise and have the potential for impact in autonomous systems. However, embedding machine learning in autonomous systems is facing significant challenges in terms of safety, robustness, and resource efficiency.
In this project, you will:
- focus on the problem of designing control policies for autonomous systems
- develop new methodologies that combine control, optimization, and machine learning tools
- demonstrate the benefits of the design methodologies in numerical simulations
Experimental evaluation may also be considered.
You will be based in the Cyber Physical Systems group with academics and researchers in the broad areas of computer engineering, embedded systems, control systems, and formal methods. The group is in the School of Electronics and Computer Science, which is highly ranked in the UK and worldwide in electrical engineering.
