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