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Self-adaptive and Self-optimising Bio-intelligent Cyber-Physical Production Systems


School of Engineering

About the Project

Bio-intelligent production is considered as an emerging area of Industry 4.0. The goal of production biologicalisation is not only to inspire production systems with biological principles or to integrate biological systems into technological systems but also to create sustainable growth via the interplay between biological and technical processes controlled through intelligent knowledge management systems. This PhD project aims to address the challenging goal of flexibility in Industry 4.0 systems via self-adaptation and self-optimisation by taking inspiration from the principles of evolutionary biology. Biological systems show successful problem-solving capabilities in complicated, complex, and unpredictable settings. They are especially useful for dealing with complex, ill-defined dynamic systems that do not have an empirical solution. Given this, the PhD student will work on design and development of novel cyber-physical system models whose components act as an independent biological entity adapting to the external disturbances and changes using bio-inspired computational intelligence. The student will have an opportunity to gain knowledge on evolutionary intelligence and swarm-based optimisation algorithms and develop a series of fully autonomous component models which will be analysed and verified using a series of agent-based computer simulations mimicking complex symbiotic production scenarios. Finally, the developed models will be deployed in a series of real-world industrial case studies derived from both manufacturing intra-plant logistics and smart agriculture domains for validation and generalisation purposes. 

The outcomes of this project for the PhD candidate are listed below:  

  • Understand key issues related to the design and deployment of cyber-physical systems, 
  • Understand the key concepts of Industry 4.0 and digital manufacturing, 
  • Gain experience in component-based system modelling and agent-based simulations, 
  • Gain experience in artificial intelligence and swarm intelligence techniques, 
  • Gain experience in modelling of intelligent embedded systems, 
  • Implement the developed systems and methods in real-world cases, 
  • Present the findings of the PhD project in international conferences, 
  • Perform high-quality research and publish it as journal articles. 

This will be a 3-year fully funded studentship for an EU/UK and overseas applicants who are keen to conduct research in smart manufacturing at LSBU in the School of Engineering.

Requirements:

  • First-class degree in Robotics/Mechanical/Cybernetics/Mechatronics/Computer Science or related scientific discipline, 
  • First rate analytical and numerical skills, with a well-rounded academic background, 
  • Expertise in relevant packages (Python and/or MATLAB), 
  • Background with artificial intelligence and evolutionary optimisation algorithms, 
  • A driven, professional, and self-dependent work attitude is essential, 
  • Experience of working within manufacturing will be an advantage, 

The ability to produce high quality presentations and written reports.


Funding Notes

We are offering a number of funded PhD scholarships. These studentships are available to UK nationals & EU citizens and overseas applicants. Those in possession of their own funding (e.g. via a non-EU government scholarship) are also welcome to apply for a place of study.

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