Robust Model-based Predictive Control for Hybrid Robotic Arms

About the Project

In contrast to conventional robots, hybrid (soft-rigid) robots offer enhanced interaction safety alongside sensorised, repeatable performance. Unlike purely soft robots, which often face challenges like parametric variation, hysteresis, and nonlinearity, hybrid robots can be equipped with sensors that provide real-time feedback on deformations. This capability enables the use of predictive algorithms for precise control. Additionally, these robots are inherently safe due to the use of soft actuators embedded within articulated joints [1, 2] , allowing them to interact with the environment with minimal risk. Robust model-based predictive control is essential to harness the safety and precision potential of hybrid robots fully. Such control strategies must handle model variations and nonlinearities that arise during real-world interactions. However, complex models can be computationally demanding, which may lower the overall control frequency, while simplified models risk inaccuracy.

This research aims to apply recent advances [3, 4] in dynamic modelling to enable robust and efficient model-based predictive control for hybrid robots. The project involves three main components: (1) developing and verifying models through simulations of hybrid robotic joints, (2) designing control algorithms using online model predictive control (MPC) techniques, and (3) extending the approach to a full hybrid robotic arm to optimise interaction and object manipulation. Throughout this project, you’ll have the opportunity to collaborate with South Korean partners as we jointly explore the development and application of hybrid robots.

Requirements:

• A background in engineering including electronic, electrical, mechanical, aerospace engineering, mechatronics and other related subjects.
• Strong programming skills in Python/C++, and some experience or knowledge of ROS (Robot Operating System) is a plus.
• Any research or internship experience is highly desirable.

About UoY

The University of York takes great pride in its ranking among the top ten UK universities in the REF, reaffirming our commitment to research excellence with a focus on social impact. As a "University for the Public Good," we are dedicated to building strong partnerships and sharing knowledge to generate both local and global benefits. The ambitious goals and potential impact of this project in Healthcare Engineering align seamlessly with our core principles of inclusion, internationalism, and collaboration.

Entry requirements:

Candidates should have (or expect to obtain) a minimum of a UK upper second-class honors degree (2.1) or equivalent in Mechanical or Electrical Engineering. Experience in biomechanics or computer science will also be considered, however, you must show an understanding of fundamental principles of experimental robotics (mechanics, dynamics and control, signal processing etc).

How to apply:

Applicants must apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/. Please read the application guidance first so that you understand the various steps in the application process. To apply, please select the PhD in Electronic Engineering. Please specify in your PhD application that you would like to be considered for this project and select supervisors.

If you have any questions about this position, please contact Dr. Babar Jamil, email: at babar.jamil@york.ac.uk.

Funding Notes

This is a self-funded project and you will need to have sufficient funds in place (eg from scholarships, personal funds and/or other sources) to cover the tuition fees and living expenses for the duration of the research degree programme. Please check the School of Physics, Engineering and Technology website for details about funding opportunities at York.