Research | Faculty of Electrical and Control Engineering at the Gdańsk University of Technology

The research and development topics carried out in the Department of Intelligent Control Systems and Decision Support (KISSiWD) mainly focus on the following issues:

• modern systems and methods of optimizing control
• modeling of processes and plants
• identification and estimation of variables and parameters
• artificial intelligence and machine learning
• data and systems analysis
• decision support systems
• optimization
• high-performance prototyping systems
• autonomous platforms 

Main application areas:

• environmental systems (wastewater treatment systems, drinking water delivery and distribution systems)
• large amount of data (big data) in particular: medical, electrical and magnetic signatures, space data 
• energy systems (nuclear power, electricity distribution systems)
• autonomous platforms and mobile robots

The main research topics are:
Modeling and identification of systems with applications to monitoring and control, among others:

• modeling of phenomena and processes occurring in the nuclear reactor and within the nuclear power plant for control and diagnostic purposes
• control of nuclear reactor power in a following the trajectory of thermal/electrical power demand, mode
• development of interval models and robust algorithms for control and estimation of biochemical processes in wastewater treatment plants and biogas plants
• solving the problem of optimal joint allocation of measuring and actuating devices in drinking water distribution systems
• modeling and identification of parameters of electrical and magnetic signatures of objects 

Synthesis and implementation of structures and algorithms for estimation and control, such as:

• hierarchical structures and algorithms of robust predictive control for complex nonlinear systems with different internal dynamics
• intelligent adaptive control and robust predictive control of linear plants with fast varying and large delays under uncertainty of model structure and parameters
• theoretical foundations and synthesis of estimation methods (system state reconstruction, soft-sensors) of process variables for control and monitoring purposes
• development of algorithms for control and navigation of autonomous platforms
• implementation and verification of advanced control and estimation algorithms in various hardware-software platforms PLC, DCS using RCP and HIL techniques

Development of artificial intelligence tools with special emphasis on deep neural networks, among others:

• development of intelligent decision support systems 
• development of methods of Explainable Aritificial Intelligence (XAI) and trustworthy artificial intelligence
• development of neural systems learning methods under a shortage of training data, with limited access to labeled data or lack of such data or knowledge of the mechanisms of the analyzed process (unsupervised learning, self-supervised learning, weakly-supervised learning, reinforcement learning)
• development of methods for searching for optimal neural network structures (NAS)

Utilization of artificial intelligence tools in diagnostics, optimization, estimation and control:

• development of methods and tools of artificial intelligence (deep neural networks, reinforcement learning), for the synthesis of effective and reliable decision support systems, in various areas of life
• exploration and analysis of multivariate process data for modeling and prediction of processes and plants, as well as detection of process anomalies and faults
• Development of methods for intelligent and reliable analysis of medical data
• development of methods of environmental monitoring with the use of intelligent soft-sensors