Smart products with focus on Cyber-Physical Systems

Whenever a close interaction is needed between cyber (i.e., computer) parts and physical parts of a product one has to deal with Cyber-Physical Systems (CPSs). In CPSs, computing processes interact closely with physical systems and humans. Examples range from networked embedded systems over manufacturing with Industry 4.0 to large-scale applications such as distributed transport systems. The effective design of dependable CPSs requires methods and tools that bring together diverse disciplines. Without these it would be difficult to gain confidence in the system-level consequences of design decisions made in any one domain, and it would be challenging to manage trade-offs between them. Thus, this WP is primarily centred around the control aspects area in order to achieve “smarter products. 

The research undertaken in this WP deals with the mathematical foundations for different formalisms enabling descriptions of CPS elements, methodologies by which CPSs efficiently can be developed and software tools that can be combined together in a chain of tools which are well-founded. Using model-based development principles challenging CPS applications will be developed in conjunction with external partners in many different application domains. It is envisaged that most of these will be around products that are mission critical and/or expensive to test in their real environments such that virtual analysis inside computer models possibly including 3D animation capabilities will be preferably. 

Based on the level of self-intelligence and self-organization, new generations of CPSs have been differentiated. Future digitalisation will require increased levels of self-regulation and self-tuning, and these pose novel challenges, need new approaches, as well as knowledge sharing and coordinated efforts. Here it is expected that self-cognizance, self-evolution and, ultimately, self-consciousness and self-reproduction will be introduced. 

In this research area, different kinds of underlying mathematics are involved. The cyber parts are typically based on discrete mathematics (logics and set theory) while the physics parts typically are most naturally modelled using differential equations. This kind of technology is necessary to enable control of systems.

Today many products are being digitalised in order to make them ”smart” such that they will be able to produce new functionality for their users. For each individual product different levels of smartness can be achieved. This forms a kind of a staircase Porter and Heppelmann as illustrated in the figure below (The "smart systems" ladder). For each of the different steps one wish to take on this staircase different enabling technologies are necessary to have success for that appropriate step. Here the four steps and their enabling technologies are:

  • Monitoring
    Here different sensors and external data sources enable monitoring of different aspects of the product (e.g. state, environment and use). Here typically the main enabling technology is the Internet of Things (IoT)
  • Control
    Here embedded software inside the product and/or in connection with a cloud solution has the ability to personalise and control some of the functionality of the product. Here typically the main enabling technology is the Cyber-Physical Systems (CPSs).
  • Optimisation: Given that one is able to manage both monitoring and control makes it possible to optimise the performance of products including pre- dictions about forthcoming issues of different kinds. Here typically the enabling technology is based on either big data analysis or machine learning.
  • Autonomy: The ultimate goal for products that today require human interaction is to achieve autonomy where configuration and coordination can be established in collaboration with external systems. Such products are typically able to optimise performance and carry out self-diagnostics in a safe manner. Here typically speciality fields such as safety, security and dependability in general are needed, and certification needs to be involved if the product has an ability to damage vital objects such as human beings.  

The “smart systems” ladder first introduced by Porter and Heppelmann in Harvard Business Review"

This research arctivity will improve the methods and tools for the development of smart products for the three highest levels in the ladder from figure above. It will follow a model-based approach and incorporate technologies in particular from machine learning and big data analysis in order to intelligently optimise the behaviour of smart systems as well as shorten the time-to-market for such products. This research will be carried out and demonstrated together with external partners primarily from industry. Research on incorporating the human-in-the-loop will also be conducted in order to determine the best approach for autonomy in different situations for example in connection with vehicles.  

There are other dimensions of smart products that will be incorporated in the work conducted in this WP. This includes the dimension of eco-systems where one needs to work with Systems of Systems (SoS) which each organisation can only decide about parts of the products that they manage themselves in a larger eco-system. The departments that are expected to able to contribute here are Department of Engineering, Department of Mathematics and in the longer-term the Physics Department.