The research on process control covers applications within oil and gas production, new energy systems, plants for CO2 capture, chemical plants, etc.
The main fields of research are optimization-based methods for control and estimation, production optimization, nonlinear control theory, parameter estimation and nonlinear state estimation/observer design.
Applications
Optimization of oil and gas production
Pulp & paper
Polymer production and other petrochemical industry
Metalurgical processes
Hydrocarbon drilling
Industrial Computer and Instrumentation Systems
The research covers theory as well as architecture and design of hardware and software necessary to implement various control and surveillance functionality with a given performance. Typically this may be a distributed solution with embedded real time application using physical input/output connected to instrumentation in various processes and devices, and an operator interface for surveillance and interaction. Safety, security, dependability, timeliness and other measures for quality of service are used as performance measures. Here the activity is described in sub areas, even though the areas are strongly interconnected and also embedded in the other activities at Engineering Cybernetics. The research activities are in several different areas:
Instrumentation
Real Time Programming
Embedded Systems
Safety, Security and Reliability
Fisheries and Aquaculture
Medical Cybernetics - Biomedical
Human Machine Interface
Applications
Safety, Security and Reliability
Machine and Process Safety
Biomechanical instrumentation
Smoke detectors
Wireless instrumentation
Software and algorithms for medical diagnostics
Wind power
Smart grids
Prosthesis control
Embedded systems for
medical applications
robotics
marine applications
Big Data Cybernetics
The research conducted in Big Data Cybernetics focuses on the development of advanced data-driven control and modelling techniques like hybrid analysis and modeling (HAM) and Reinforcement Learning, that bridge the fields of artificial intelligence and physics/knowledge-based methods. One aim is to enable emerging AI technologies to gain fairness, accountability, transparency, explainability and trustworthiness. Another aim is to introduce state-of-the-art AI methods in cybernetics in novel ways.
